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Performance Assessment of Vessel Dynamic Models for Long-Term Prediction Using Heterogeneous Data
Abstract
Ship traffic monitoring is a foundation for many maritime security domains, and monitoring system specifications underscore the necessity to track vessels beyond territorial waters. However, vessels in open seas are seldom continuously observed. Thus, the problem of long-term vessel prediction becomes crucial. This paper focuses attention on the performance assessment of the Ornstein-Uhlenbeck (OU) model for long-term vessel prediction, as compared to usual and well-established nearly constant velocity (NCV) model. Heterogeneous data, such as Automatic Identification System (AIS) data, high-frequency surface wave radar data, and synthetic aperture radar data, are exploited to this aim. Two different association procedures are also presented to cue dwells in case of gaps in the transmission of AIS messages. Suitable metrics have been introduced for the assessment. Considerable advantages of the OU model are pointed out with respect to the NCV model.
... M ARITIME traffic monitoring mostly relies on data collected by heterogeneous sensor systems, including, e.g., the self-reporting Automatic Identification System (AIS) [1]- [3], coastal radars [4]- [9], space-borne sensors devices such as Synthetic Aperture Radar (SAR) [10], video and infrared cameras [11]. Stealth activities [12], in which the perpetrators aim to remain hidden and undetected by law-enforcement bodies throughout the whole duration of the activity, are among the main issues to deal with. ...
... It is worth mentioning that, different from the AIS data, which contain vessel labeling information, other (especially non-collaborative) sensors (e.g. radar) suffer from the measurement-origin uncertainty [10]. In this work we assume that the association of contacts to the vessel of interest is solved in a preliminary stage, see e.g. ...
... In this work we assume that the association of contacts to the vessel of interest is solved in a preliminary stage, see e.g. [10], [25]. The possible association error, relevant when several multiple targets are close to each other (uncommon scenario in open sea), is neglected and left to future investigation. ...
A novel anomaly detection procedure based on the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process is presented. The considered anomaly is a vessel that deviates from a planned route, changing its nominal velocity
. In order to hide this behavior, the vessel switches off its Automatic Identification System (AIS) device for a time
T
, and then tries to revert to the previous nominal velocity
. The decision that has to be taken is either declaring that a deviation happened or not, relying only upon two consecutive AIS contacts. Furthermore, the extension to the scenario in which multiple contacts (e.g. radar) are available during the time period
T
is also considered. A proper statistical hypothesis testing procedure that builds on the changes in the OU process long-term velocity parameter
of the vessel is the core of the proposed approach and enables the solution of the anomaly detection problem. Closed analytical forms are provided for the detection and false alarm probabilities of the hypothesis test.
... The resulting graph can be considered a synthetic representation of the traffic under normal circumstances, that can be further exploited by tracking, data fusion or anomaly detection algorithms. For the motion model, we rely on the Ornstein-Uhlenbeck (OU) stochastic process, which has been shown [1]- [3] to be a realistic model for the dynamics of the velocity of ships in open sea. The proposed approach, shown in Fig. 2, encompasses several steps: from the detection of way-points, to the graph construction, to the assessment of its performance. ...
... More recently, mean-reverting stochastic processes has been proposed to model the velocity of ships in open sea and the approach has been validated against a large real-world data set [1]. This modelling, which is also relevant to types of data other than AIS [3], finds application for anomaly detection [17] too, while its use for traffic pattern discovery is documented in this work and more thoroughly in [4]. ...
... Specifically, we selected the intervals minP oints ∈ [3,50] and ∈ [0.01, 0.2]. The choice of the clustering parameters is usually the result of a trade-off between graph resolution and fragmentation. ...
Inspired by the fair regularity of the motion of ships, we present a method to derive a representation of the commercial maritime traffic in the form of a graph, whose nodes
represent way-point areas, or regions of likely direction changes, and whose edges represent navigational legs with constant cruise velocity. The proposed method is based on the representation of a ship’s velocity with an Ornstein-Uhlenbeck process and on the
detection of changes of its long-run mean to identify navigational way-points. In order to assess the graph representativeness of the traffic, two performance metrics are introduced, leading to distinct graph construction criteria. Finally, the proposed method
is validated against real-world Automatic Identification System data collected in a large area.
... More recently, we proposed a novel maritime anomaly detection technique [24], [25] that reveals possible path deviations during intentional disablements of the AIS transponder or during periods of AIS unavailability. Specifically, the detector is based on a statistical test to identify changes in a parameter of the Ornstein-Uhlenbeck (OU) stochastic process that models the ship velocity, which was shown [27]- [29] to be a realistic representation of vessels' dynamics in open sea. ...
... , N , thus T = N ∆t, and sufficiently large with respect to the OU parameters, in order to guarantee that the process reaches a steady state behavior in each time interval. Otherwise stated, it is assumed that the position covariance matrix at time n∆t, given the position at (n − 1)∆t, is proportional to ∆t, and the transitory terms are neglected (∆t γ −1 x,y ), i.e., C p (∆t) = ∆tC 0 , where C 0 is obtained considering the terms proportional to t in equations (29), (30), (31) and (32) in Appendix A. ...
In principle, the Automatic Identification System (AIS) makes covert rendezvous at sea, such as smuggling and piracy, impossible; in practice, AIS can be spoofed or simply
disabled. Previous work showed a means whereby such deviations can be spotted. Here we play the opponent’s side, and describe the least-detectable trajectory that that the elusive vessel can take. The opponent’s route planning problem is formalized as a nonconvex optimization problem capitalizing the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. The velocity of the vessel is modeled with an Ornstein-Uhlenbeck (OU) mean reverting stochastic process, and physical and practical requirements are accounted for by enforcing several constraints at the optimization design stage. To handle the resulting nonconvex optimization problem, we propose a globally-optimal
and computationally-efficient technique, called the Non-Convex Optimized Stealth Trajectory (N-COST) algorithm. The N-COST algorithm consists in solving multiple convex problems, whose number is proportional to the number of segments of the piecewise
OU trajectory. The effectiveness of the proposed approach is demonstrated through case studies and a real-world example.
... More recently, a novel maritime anomaly detector [8], [9], has been proposed to reveal possible path deviations during an intentional disablement of the AIS transponder or during a period without data available, relying only on the available measurements. Specifically, this detector is based on a hypothesis testing procedure able to identify changes in the longrun mean velocity parameter of the Ornstein-Uhlenbeck (OU) process, which was shown [10]- [12] to be a realistic model for vessels' dynamics in open sea. ...
... , t m K }, h = 1, . . . , K i , and t m i,h = t m i,k , h = k; • H i and C i depend on the available measurements (see equations (12) and (13), respectively); ...
A new methodology is proposed to deceive an anomalous trajectory detector by designing ship paths that deviate from the nominal traffic routes in an optimized way. The route planning is formalized as a min-max problem (with respect to surveillance system acquisition instants) focusing on the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. Modeling the vessel's dynamic according to the Ornstein-Uhlenbeck (OU) mean-reverting stochastic process , physical, practical, and kinematic requirements are also accounted for forcing several constraints at the design stage. A computationally efficient technique is proposed to handle the resulting non-convex optimization problem, and some case studies are reported to assess its effectiveness.
... Unlike previous work on maritime situational awareness, here the anomaly detection problem is addressed relying on a novel Bayesian random finite set (RFS) filter [9] that builds on the changes in the OU process long-term mean velocity of the object. The Ornstein-Uhlenbeck process has been shown [10][11][12] to better model the behavior of real-world targets, such as marine vessels, with respect to conventional models including the nearly-constant velocity (NCV) [13]. In this framework, the use of the OU model turns out to be a valuable tool to represent any deviation from the nominal motion as an unknown input affecting the object dynamics. ...
... A novel method [10], based on the Ornstein-Uhlenbeck meanreverting stochastic process, has been lately proposed to address the problem of long-term object prediction. This model has been shown [10][11][12] to be a realistic model of ships' movements in open sea which reduces by orders of magnitude the uncertainty region related to the predicted object position with respect to state-of-the-art models. The main difference between the OU process and other well-established models is a feedback loop which ensures that the velocity of the object does not diverge over time, but is instead bounded around a finite value representing the desired (cruise) velocity of the target. ...
... The OU process has been shown to be better suited to model the behavior of a significant portion of real-world vessel trajectories than respect to conventional models [5]- [11]. In this framework, the use of the OU model turns out to be a valuable tool when vessel information is not available, providing a good estimation of a ship's position and velocity, even after several hours. ...
... assuming the independence of the terms in (11). ...
... The approach herein set forth is inspired by the works presented in [1]- [3], where the validity of the Ornstein-Uhlenbeck (OU) process to model vessel dynamics in open sea is demonstrated in terms of performance improvement compared to the more commonly adopted Nearly-Constant Velocity (NCV) model. ...
... Performing the cluster analysis with DBSCAN in a four-dimensional space instead of in a six-dimensional one is important not only from a computational cost perspective, but also because it helps to mitigate the curse of dimensionality issue, i.e. there is little difference in the distances between pair of samples as the number of coordinates increases when a measure like the Euclidean distance is used, making very difficult to find an appropriate value for ǫ.3 Circular distributions, such as the Von Mises distribution, can also be considered since the random variables are angles. However, the Gaussian approximations' tails would not be a problem and can be neglected due to the variance of a few degrees. ...
We propose an unsupervised procedure to automatically extract a graph-based model of commercial maritime traffic routes from historical Automatic Identification System (AIS) data. In the proposed representation, the main elements of maritime traffic patterns, such as maneuvering regions and sea-lanes, are represented, respectively, with graph vertices and edges. Vessel motion dynamics are defined by multiple Ornstein-Uhlenbeck (OU) processes with different long-run mean parameters , which in our approach can be estimated with a change detection procedure based on Page's test, aimed to reveal the spatial points representative of velocity changes. A density-based clustering algorithm (DBSCAN) is then applied to aggregate the detected changes into groups of similar elements and reject outliers. To validate the proposed graph-based representation of the maritime traffic, two performance criteria are tested against a real-world trajectory data set collected off the Iberian Coast and the English Channel. Results show the effectiveness of the proposed approach, which is suitable to be integrated at any level of a JDL system.
... 6 Select all connected items. By analyzing the strength of collaboration among authors, we find that Willett, Braca, and Liu frequently collaborate in the field of intelligent shipping [18][19][20]. Meanwhile, Li and Xiao have also made significant contributions to the integration and optimization of intelligent shipping systems [21][22][23][24]. ...
Amid the dual imperatives of global trade expansion and low-carbon transition, intelligent maritime shipping has emerged as a central driver for the innovation of international logistics systems, now entering a critical window period for the deep integration of Internet technologies and automated port infrastructure. While existing research predominantly focuses on isolated applications of intelligent technologies, systematic evaluations of the synergistic effects of technological integration on maritime ecosystems, policy compatibility, and contributions to global carbon emission governance remain under-explored. Leveraging bibliometric analysis, this study systematically examines 488 publications from the Web of Science (WoS) Core Collection (2000–2024), yielding three pivotal findings: firstly, China dominates the research landscape, with a 38.5% contribution share, where Artificial Intelligence (AI), the Internet of Things (IoT), and port automation constitute the technological pillars. However, critical gaps persist in cross-system protocol standardization and climate-adaptive modeling, accounting for only 2.7% and 4.2% of the literature, respectively. Secondly, international collaboration networks exhibit pronounced “Islamization”, characterized by an inter-team collaboration rate of 17.3%, while the misalignment between rapid technological iteration and existing maritime regulations exacerbates industry risks. Thirdly, a dual-track pathway integrating Cyber–Physical System (CPS)-based digital twin ports and open-source vertical domain-specific large language models is proposed. Empirical evidence demonstrates its efficacy in reducing cargo-handling energy consumption by 15% and decision-making latency by 40%. This research proposes a novel tripartite framework, encompassing technological, institutional, and data sovereignty dimensions, to resolve critical challenges in integrating multi-source maritime data and managing cross-border governance. The model provides academically validated and industry-compatible strategies for advancing sustainable maritime intelligence. Subsequent investigations should expand data sources to include regional repositories and integrate interdisciplinary approaches, ensuring the adaptability of both technical systems and international policy coordination mechanisms across diverse maritime ecosystems.
... Moreover, a wide range of methods have been implemented for ship trajectory prediction or route extrapolation in the literature. For example, algorithms from the tracking community include the Nearly Constant Velocity (NCV) model as well as the Ornstein-Uhlenbeck (OU) model for long-term vessel prediction [9]. In contrast to a model-based approach, data-driven methods involving deep learning models such as Recurrent Neural Networks (RNNs) are becoming more popular [8,10]. ...
... Moreover, a wide range of methods have been implemented for ship trajectory prediction or route extrapolation in the literature. For example, algorithms from the tracking community include the Nearly Constant Velocity (NCV) model as well as the Ornstein-Uhlenbeck (OU) model for long-term vessel prediction [9]. In contrast to a model-based approach, data-driven methods involving deep learning models such as Recurrent Neural Networks (RNNs) are becoming more popular [8,10]. ...
This research proposes an anomaly detection workflow intended primarily for integration into satellite-based tip and cue services for maritime surveillance. The workflow is centred around a ship trajectory prediction model which is designed to respond to anomalous events such as AIS "shut-off" events. This is important for accurately predicting the trajectories of potentially suspicious vessels that are moving, and subsequently scheduling the tasking of satellite acquisitions to monitor these vessels. The research implements a ship trajectory prediction model based on AIS data using Recurrent Neural Networks (RNNs). The efficacy of the model is evaluated based on the mean great circle distance between predicted and actual vessel trajectories, demonstrating satisfactory performance for typical motion patterns while acknowledging certain limitations in prediction accuracy. Overall, this study represents a significant step forward in the integration of imaging satellites and AIS data for maritime surveillance, offering a promising approach for anomaly detection and improving the efficiency of satellite-based monitoring systems. A GitHub repository containing the source code and related materials for this work is made available.
... This method utilizes fuzzy membership to quantify the degree of association between trajectories and employs dual-threshold detection to determine associated trajectory pairs [8]. In addition to AIS and SWR data, synthetic aperture radar (SAR) data and satellite images are employed to facilitate trajectory association for the objective of ship traffic monitoring in open seas [9,10]. With the advancement of deep learning, relevant techniques have also been applied to ship trajectory association. ...
Intelligent navigation is a crucial component of intelligent ships. This study focuses on the situational awareness of intelligent navigation in inland waterways with high vessel traffic densities and increased collision risks, which demand enhanced vessel situational awareness. To address perception data association issues in situational awareness, particularly in scenarios with winding waterways and multiple vessel encounters, a method based on trajectory characteristics is proposed to determine associations between Automatic Identification System (AIS) and radar objects, facilitating the fusion of heterogeneous data. Firstly, trajectory characteristics like speed, direction, turning rate, acceleration, and trajectory similarity were extracted from ship radar and AIS data to construct labeled trajectory datasets. Subsequently, by employing the Support Vector Machine (SVM) model, we accomplished the discernment of associations among the trajectories of vessels collected through AIS and radar, thereby achieving the association of heterogeneous data. Finally, through a series of experiments, including overtaking, encounters, and multi-target scenarios, this research substantiated the method, achieving an F1 score greater than 0.95. Consequently, this study can furnish robust support for the perception of intelligent vessel navigation in inland waterways and the elevation of maritime safety.
... Marine target recognition is an important means for maritime rescue (Soon et al. 2018), iceberg detection (Andrade et al. 2016), territorial water security (Vivone et al. 2017), and man-made target search (De Lima Filho et al. 2022) in the natural environment. Optical systems play the role of eyes in sea surface target detection, where the most intuitive effect is visible light imaging. ...
The performance of sole optical imaging device for marine target recognition is degraded by sea fog, sea-glint, and many other disturbing factors. To enhance the ability of target recognition in marine environment, we propose a polarization-intensity joint imaging method and the corresponding processing method. We combine fine imaging of polarization in a small field of view with wide-field imaging of visible light intensity, using visible light intensity information for large-scale target surveys. After locking onto areas of interest, we utilize high-resolution polarization cameras with small fields of view for detailed inspection. We enhance and fuse the information from areas of interest using the proposed matching information processing method. Meanwhile, to deal with problems existing in the marine environment polarization-intensity joint imaging, such as details loss in DoP (degree of polarization) image, low target resolution in the large field of view intensity image, and insufficient information in a single image, etc., we extract the details of raw image from DoFP (division-of-focal-plane) polarization camera as residual compensation for DoP image super-resolution and cooperate with the RealSR algorithm to super-resolve the local target details of the intensity image in a large field of view. On the premise that PIQE (Perception-based Image Quality Evaluator) reaches the excellent score range, the images with the same resolution are fused. The automatic recognition comparison before and after processing proves that the accuracy of target recognition can be effectively improved after processing.
... High-resolution satellite videos realize shortdated gaze observation of the designated area on the ground, and its emergence has improved the temporal resolution of remote-sensing data to the second level. For satellite video interpretation, SOT is an important step in dynamic information extraction, and it is also the basis and prerequisite for estimating traffic density (Kopsiaftis and Karantzalos 2015), motion analysis (Lu et al. 2018;Thomas, Kambhamettu, and Geiger 2011), and surveillance (Vivone et al. 2017). ...
High-resolution satellite videos realize the short-dated gaze observation of the designated area on the ground, and its emergence has improved the temporal resolution of remote sensing data to the second level. Single object tracking (SOT) task in satellite video has attracted considerable attention. However, it faces challenges such as complex background, poor object feature representation, and lack of publicly available datasets. To cope with these challenges, a ThickSiam framework consisting of a Thickened Residual Block Siamese Network (TRBS-Net) for extracting robust semantic features to obtain the initial tracking results and a Remoulded Kalman Filter (RKF) module for simultaneously correcting the trajectory and size of the targets is designed in this work. The results of TRBS-Net and RKF modules are combined by an N-frame-convergence mechanism to achieve accurate tracking results. Ablation experiments are implemented on our annotated dataset to evaluate the performance of the proposed ThickSiam framework and other 19 state-of-the-art trackers. The comparison results show that our ThickSiam tracker obtains a precision value of 0.991 and a success value of 0.755 while running at 56.849 FPS implemented on one NVIDIA GTX1070Ti GPU.
... Over the last few years, AIS data have been extensively used in research for validation purposes as "groundtruth" information, e.g., in maritime surveillance with coastal radars [42][43][44] as confirmation tracks for radar detections, or for the validation of a target motion model for long-term ship prediction 21 . AIS has also been used to show how the data association can be significantly improved using the long-term prediction and combining AIS with HF Surface Wave radar (HFSWR) data, or Synthetic Aperture Radar (SAR) data 45 . But in any case, there is a significant literature that considers AIS the sole source of information for maritime surveillance 21,45-49 , port www.nature.com/scientificreports/ ...
To prevent the outbreak of the Coronavirus disease (COVID-19), many countries around the world went into lockdown and imposed unprecedented containment measures. These restrictions progressively produced changes to social behavior and global mobility patterns, evidently disrupting social and economic activities. Here, using maritime traffic data collected via a global network of Automatic Identification System (AIS) receivers, we analyze the effects that the COVID-19 pandemic and containment measures had on the shipping industry, which accounts alone for more than 80% of the world trade. We rely on multiple data-driven maritime mobility indexes to quantitatively assess ship mobility in a given unit of time. The mobility analysis here presented has a worldwide extent and is based on the computation of: Cumulative Navigated Miles (CNM) of all ships reporting their position and navigational status via AIS, number of active and idle ships, and fleet average speed. To highlight significant changes in shipping routes and operational patterns, we also compute and compare global and local vessel density maps. We compare 2020 mobility levels to those of previous years assuming that an unchanged growth rate would have been achieved, if not for COVID-19. Following the outbreak, we find an unprecedented drop in maritime mobility, across all categories of commercial shipping. With few exceptions, a generally reduced activity is observable from March to June 2020, when the most severe restrictions were in force. We quantify a variation of mobility between −5.62 and −13.77% for container ships, between +2.28 and −3.32% for dry bulk, between −0.22 and −9.27% for wet bulk, and between −19.57 and −42.77% for passenger traffic. The presented study is unprecedented for the uniqueness and completeness of the employed AIS dataset, which comprises a trillion AIS messages broadcast worldwide by 50,000 ships, a figure that closely parallels the documented size of the world merchant fleet.
... Given the object state in the first frame, the tracker aims to predict the object state in the following frames. Besides, aerial tracking has derived a wide range of applications including, but not limited to, motion object analysis [2], [3], geographical survey [4], and surveillance [5]. Despite impressive achievements, designing an efficient and accurate tracker for aerial tracking remains a challenging task due to the special challenges aroused by flight processing such as fast motion, long-term tracking, and low resolution. ...
Object tracking approaches based on the Siamese network have demonstrated their huge potential in the remote sensing field recently. Nevertheless, due to the limited computing resource of aerial platforms and special challenges in aerial tracking, most existing Siamese-based methods can hardly meet the real-time and state-of-the-art performance simultaneously. Consequently, a novel Siamese-based method is proposed in this work for onboard real-time aerial tracking, i.e., SiamAPN. The proposed method is a no-prior two-stage method, i.e., Stage-1 for proposing adaptive anchors to enhance the ability of object perception and Stage-2 for fine-tuning the proposed anchors to obtain accurate results. Distinct from the traditional predefined anchors, the proposed anchors can adapt automatically to the tracking object. Besides, the internal information of adaptive anchors is utilized to feedback SiamAPN for enhancing the object perception. Attributing to the feature fusion network, different semantic information is integrated, enriching the information flow that is significant for robust aerial tracking. In the end, the regression and multiclassification operation refine the proposed anchors meticulously. Comprehensive evaluations on three well-known aerial tracking benchmarks have proven the superior performance of the presented approach. Moreover, to verify the practicability of the proposed method, SiamAPN is implemented onboard a typical embedded aerial tracking platform to conduct the real-world evaluations on specific aerial tracking scenarios, e.g., fast motion, long-term tracking, and low resolution. The results have demonstrated the efficiency and accuracy of the proposed approach, with a processing speed of over 30 frames/s. In addition, the image sequences in the real-world evaluations are collected and annotated as a new aerial tracking benchmark, i.e., UAVTrack112.
... In research community, many works have been conducted to investigate the multi-source data fusion technologies such as radar and AIS data fusion [7], [43]- [45] and information fusion of AIS, radar and SAR data for maritime surveillance [46], [47]. Ornstein-Uhlenbeck (OU) process is one of the techniques to improve association of AIS with HFSW radars and SAR data [48], whose recent advances are documented in [49] and [50] where a novel scalable data fusion paradigm based on message passing or, more concretely, the loopy sum-product algorithm is proposed. This approach outperforms the state-ofthe-art algorithms in terms of the estimation accuracy, the computational complexity and implementation flexibility, making it useful for application scenarios that require real-time operations on resource limited devices. ...
Maritime traffic service networks and information systems play a vital role in maritime traffic safety management. The data collected from the maritime traffic networks are essential for the perception of traffic dynamics and predictive traffic regulation. This paper is devoted to surveying the key processing components in maritime traffic networks. Specifically, the latest progress on maritime traffic data mining technologies for maritime traffic pattern extraction and the recent effort on vessels' motion forecasting for better situation awareness are reviewed. Through the review, we highlight that the traffic pattern knowledge presents valued insights for wide-spectrum domain application purposes, and serves as a prerequisite for the knowledge based forecasting techniques that are growing in popularity. The development of maritime traffic research in pattern mining and traffic forecasting reviewed in this paper affirms the importance of advanced maritime traffic studies and the great potential in maritime traffic safety and intelligence enhancement to accommodate the implementation of the Internet of Things, artificial intelligence technologies, and knowledge engineering and big data computing solution.
... Under this prism, the prediction task is an instantiation of the pattern that most fits to the short history of the object's movement (the 'tail' of its trajectory so far). State-of-the-art methods in this area include, at least, NextLocation [34], MyWay [96], Ornstein-Uhlenbeck (OU) stochastic process [126] [127]. -Semantic-aware approaches involve semantics extracted by the surrounding environment (whether e.g. a stay is at home or at a park), build patterns on top of this knowledge, and apply them for prediction of the next location(s). ...
The tremendous growth of positioning technologies and GPS enabled devices has produced huge volumes of tracking data during the recent years. This source of information constitutes a rich input for data analytics processes, either offline (e.g. cluster analysis, hot motion discovery) or online (e.g. short-term forecasting of forthcoming positions). This paper focuses on predictive analytics for moving objects (could be pedestrians, cars, vessels, planes, animals, etc.) and surveys the state-of-the-art in the context of future location and trajectory prediction. We provide an extensive review of over 50 works, also proposing a novel taxonomy of predictive algorithms over moving objects. We also list the properties of several real datasets used in the past for validation purposes of those works and, motivated by this, we discuss challenges that arise in the transition from conventional to Big Data applications. CCS Concepts: Information systems > Spatial-temporal systems; Information systems > Data analytics; Information systems > Data mining; Computing methodologies > Machine learning Additional Key Words and Phrases: mobility data, moving object trajectories, trajectory prediction, future location prediction.
... Heterogeneous data, such as automatic identification system (AIS) data, high-frequency surface wave (HFSW) radar data, and synthetic aperture radar (SAR) data, have been exploited in research for maritime surveillance purposes [32]. In our case, two sources of information were fused to support the outlier detection process: OTH radar and AIS data. ...
Detection of outliers in radar signals is a considerable challenge in maritime surveillance applications. High-Frequency Surface-Wave (HFSW) radars have attracted significant interest as potential tools for long-range target identification and outlier detection at over-the-horizon (OTH) distances. However, a number of disadvantages, such as their low spatial resolution and presence of clutter, have a negative impact on their accuracy. In this paper, we explore the applicability of deep learning techniques for detecting deviations from the norm in behavioral patterns of vessels (outliers) as they are tracked from an OTH radar. The proposed methodology exploits the nonlinear mapping capabilities of deep stacked autoencoders in combination with density-based clustering. A comparative experimental evaluation of the approach shows promising results in terms of the proposed methodology’s performance.
Cybersecurity is becoming an increasingly important aspect in ensuring maritime data protection and operational continuity. Ships, ports, surveillance and navigation systems, industrial technology, cargo, and logistics systems all contribute to a complex maritime environment with a significant cyberattack surface. To that aim, a wide range of cyberattacks in the maritime domain are possible, with the potential to infect vulnerable information and communication systems, compromising safety and security. The use of navigation and surveillance systems, which are considered as part of the maritime OT sensors, can improve maritime cyber situational awareness. This survey critically investigates whether the fusion of OT data, which are used to provide maritime situational awareness, may also improve the ability to detect cyberincidents in real time or near-real time. It includes a thorough analysis of the relevant literature, emphasizing RF but also other sensors, and data fusion approaches that can help improve maritime cybersecurity.
We study the performance of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say , where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and the related error rate I depend on the given training set. The conditions for the exponential convergence can be verified and tested numerically exploiting the available dataset or a synthetic dataset generated according to the underlying statistical model. Coherently with the large deviations theory, we can also establish the convergence of the normalized D3F statistic to a Gaussian distribution. Furthermore, approximate error probability curves are provided, thanks to the refined asymptotic derivation, where represents the most representative sub-exponential terms of the error probabilities. Leveraging the refined asymptotic, we are able to compute an accurate analytical approximation of the classification performance for both the regimes of small and large values of n. Theoretical findings are corroborated by extensive numerical simulations and by the use of real-world data, acquired by an X-band maritime radar system for surveillance.
We study the performance -- and specifically the rate at which the error probability converges to zero -- of Machine Learning (ML) classification techniques. Leveraging the theory of large deviations, we provide the mathematical conditions for a ML classifier to exhibit error probabilities that vanish exponentially, say , where n is the number of informative observations available for testing (or another relevant parameter, such as the size of the target in an image) and I is the error rate. Such conditions depend on the Fenchel-Legendre transform of the cumulant-generating function of the Data-Driven Decision Function (D3F, i.e., what is thresholded before the final binary decision is made) learned in the training phase. As such, the D3F and, consequently, the related error rate I, depend on the given training set, which is assumed of finite size. Interestingly, these conditions can be verified and tested numerically exploiting the available dataset, or a synthetic dataset, generated according to the available information on the underlying statistical model. In other words, the classification error probability convergence to zero and its rate can be computed on a portion of the dataset available for training. Coherently with the large deviations theory, we can also establish the convergence, for n large enough, of the normalized D3F statistic to a Gaussian distribution. This property is exploited to set a desired asymptotic false alarm probability, which empirically turns out to be accurate even for quite realistic values of n. Furthermore, approximate error probability curves are provided, thanks to the refined asymptotic derivation (often referred to as exact asymptotics), where represents the most representative sub-exponential terms of the error probabilities.
The Automatic Identification System (AIS) is an essential and economical equipment for collision avoidance and maritime surveillance. However, AIS can be subject to intentional reporting of false information, or "spoofing". This paper assumes the vessel trajectory nominally follows a piece-wise mean-reverting process, thereby, it addresses the problem of establishing whether a vessel is reporting adulterated position information through AIS messages in order to hide its current planned route and a possible deviation from the nominal route. Multiple hypothesis testing suggests a framework to enlist reliable information from monitoring systems (coastal radars and space-born satellite sensors) in support of detection of both anomalies, spoofing and stealth deviations. The proposed solution involves the derivation of anomaly detection rules based on the Generalized Likelihood Ratio Test (GLRT) and the Model Order Selection (MOS) methodologies. The effectiveness of the proposed anomaly detection strategy is tested for different case studies within an operational scenario with simulated data.
The huge volumes of spatial-temporal maritime traffic data that is currently being accumulated from the Automatic Identification System (AIS) can open the path for smart decision-making and facilitate a number of intelligent applications including ship movements tracking, route patterns extraction, operations and next event prediction, trade and cargo flow, etc. However, it is very challenging to store, transfer, and load such large volumes of spatial-temporal data. Pre-analysis is essential before extracting spatial and temporal relationships in maritime traffic data, in which data variability, inconsistent data quality, and computational complexity demanded by various applications can pose additional constraints. In this chapter, we first briefly review maritime traffic surveillance systems for spatial-temporal data collection. Then we present a computational framework to efficiently compress, transfer, and acquire necessary information for the further analysis of large-scale AIS data to empower relevant applications in the maritime sector. The framework is composed of two parts: the first is a lossless compression algorithm that compresses the AIS data into binary form for efficient storage, speedy access, and easy transfer across networks and systems within the organization; the second is an aggregation algorithm which derives movement and activity information of vessels grouped by grid and/or time window from the compressed binary files. The aggregation algorithm compresses and organizes data by vessel identity (ID), thereby improving accessibility and reducing storage demand. Finally, a use case of maritime big data intelligent surveillance is briefly described.
Satellite video target tracking is a new topic in the remote sensing field, which refers to tracking moving objects of interest from satellite video in real time. The target of interest usually occupies only a few pixels in a satellite video image, even when the train is long. Thus, satellite video target tracking still faces new challenges compared with traditional visual tracking, including the detection of low-resolution targets, features with less representation, and targets with an extremely similar background. Little research has been done on satellite video target tracking, and little is known about whether or not the existing tracking algorithms can still work on the satellite video data. This paper, for the first time, intensively investigated 13 typical trackers in traditional visual tracking. The experimental results suggest that most of the state-of-the-art tracking algorithms mainly rely on luminance, color features, or convolutional features, and they fail to track satellite video targets due to their inadequate representation features. To overcome this difficulty, we propose a velocity correlation filter (VCF) algorithm, which employs both a velocity feature and an inertia mechanism (IM) to construct a specific kernel correlation filter for the satellite video target tracking. The velocity feature has a high discriminative ability to detect moving targets in satellite videos, and the IM can prevent model drift adaptively. Experimental results on three real satellite video data sets show that the VCF outperforms state-of-the-art tracking methods with regard to precision and success plots while running at over 100 frames per second.
Understanding maritime traffic patterns is key to Maritime Situational Awareness applications, in particular, to classify and predict activities. Facilitated by the recent build-up of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, ship movement information is becoming increasingly available, both in coastal areas and open waters. The resulting amount of information is increasingly overwhelming to human operators, requiring the aid of automatic processing to synthesize the behaviors of interest in a clear and effective way. Although AIS data are only legally required for larger vessels, their use is growing, and they can be effectively used to infer different levels of contextual information, from the characterization of ports and off-shore platforms to spatial and temporal distributions of routes. An unsupervised and incremental learning approach to the extraction of maritime movement patterns is presented here to convert from raw data to information supporting decisions. This is a basis for automatically detecting anomalies and projecting current trajectories and patterns into the future. The proposed methodology, called TREAD (Traffic Route Extraction and Anomaly Detection) was developed for different levels of intermittency (i.e., sensor coverage and performance), persistence (i.e., time lag between subsequent observations) and data sources (i.e., ground-based and space-based receivers).
This document describes the derivation of the backscattering coefficient in ESA ERS SAR products.
Maritime anomaly detection requires an efficient representation and consistent knowledge of vessel behaviour. Automatic Identification System (AIS) data provides ships state vector and identity information that is here used to automatically derive knowledge of maritime traffic in an unsupervised way. The proposed approach only utilises AIS data, historical or real-time, and is aimed at incrementally learning motion patterns without any specific a priori contextual description. This can be applied to a single AIS terrestrial receiver, to regional networks or to global scale tracking. The maritime traffic representation underpins low-likelihood behaviour detection and supports enhanced Maritime Situational Awareness by providing a characterisation of vessels traffic.
The measurement selection for updating the state estimate of a target's track, known as data association, is essential for good performance in the presence of spurious measurements or clutter. A classification of tracking and data association approaches has been presented, as a pure MMSE approach, which amounts to a soft decision, and single best-hypothesis approach, which amounts to a hard decision. It has been shown that the optimal state estimator in the presence of data association uncertainty consists of the computation of the conditional pdf of the state x(k) given all information available at time k, namely, the prior information about the initial state, the intervening known inputs, and the sets of measurements through time k. It has also been pointed out that if the exact conditional pdf, which is a mixture, is available, then its recursion requires only the probabilities of the most recent association events. The conditions under which this result holds, namely whiteness of the noise, detection, and clutter processes, were presented. The PDAF and JPDAF algorithms, which carry out data association and state estimation in clutter, have been described. A simple example was given to illustrate how the clutter and occasional missed detections can lead to track loss for a standard tracking filter, and how PDAF can keep the target in track under such circumstances. By using the Monte Carlo in a simulated based surveillance as an exampled shown. The numerous applications of the PDAF/JPDAF illustrated in "Real-World Applications of PDAF and JPDAF" show the potential pitfalls of using sophisticated algorithms for tracking in difficult environments as well as how to overcome them. The effect of finite sensor resolution can be a more severe problem than the data association and deserves special attention.
The paper is devoted to statistical analysis of vessel motion patterns in the ports and waterways using AIS ship self-reporting data. From the real historic AIS data we extract motion patterns which are then used to construct the corresponding motion anomaly detectors. This is carried out in the framework of adaptive kernel density estimation. The anomaly detector is then sequentially applied to the real incoming AIS data for the purpose of anomaly detection. Under the null hypothesis (no anomaly), using the historic motion pattern data, we predict the motion of vessels using the Gaussian sum tracking filter.
In this paper we study the problem of estimating the long-run mean of the Ornstein-Uhlenbeck (OU) stochastic process and its effect on the long-term prediction of future vessel states, which is a crucial problem for Maritime Situational Awareness (MSA). We employ a Sample Mean Estimator (SME) to estimate the key Ornstein-Uhlenbeck (OU) parameter from the observations, computing the closed-form SME covariance error in both the random and constant sampling time regimes, providing a fundamental building block of the overall long-term state prediction covariance. We show also that the SME is: √ n-consistent when the sampling time is random; asymptotically efficient when the sampling time is constant; and very close to the Cramér-Rao Lower Bound (CRLB) in the cases of practical interest for MSA.
Long-term target state estimation of non-maneuvering targets, such as vessels under way in open sea, is crucial for maritime security. The dynamics of non-maneuvering targets is traditionally modeled with a white noise random process on the velocity, which is assumed to be nearly-constant. We show that this model might be an implausible hypothesis for a significant portion of maritime ship traffic, as vessels under way tend to adjust their speed continuously around a desired value. Additionally, vessels will naturally seek to optimize fuel consumption. We developed a method to predict long-term target states based on mean-reverting stochastic processes. Specifically, we use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation and to a completely different time scaling law for the related uncertainty, which in the long term is shown to be orders of magnitude lower than nearly-constant velocity assumption. The proper modeling provides some improvement in accuracy; but the real benefit is improved track-stitching when there are lengthy gaps in observability. In support of the proposed model, we propose a large-scale analysis of a significant portion of the real-world maritime traffic in the Mediterranean Sea.
A second-order Ornstein-Uhlenbeck (OU) Process, or Mixed OU (MOU) process, provides a stable and stationary generalization to the well-established nearly-constant velocity (NCV) model – the workhorse kinematic model used in the target tracking community. The MOU process is useful in many settings including long time-horizon simulations, multiple-model filtering of evasive targets, and hypothesis aggregation for improved track extraction. This paper clarifies the discrete-time target state covariance at birth and its relationship to the underlying continuous-time model. Further, we suggest additional fruitful applications of the MOU process including long-horizon prediction and ground-constrained tracking. We extend the MOU model to these context-aware settings and provide some evidence of its potential. Keywords—multi-target tracking (MTT); Ornstein Uhlenbeck (OU); Integrated Ornstein Uhlenbeck (IOU); Mixed Ornstein Uhlenbeck (MOU); mean-reverting MOU (MR-MOU); multiple-hypothesis tracking (MHT).
We present a novel method for predicting long-term target states based on mean-reverting stochastic processes. We use the Ornstein-Uhlenbeck (OU) process, leading to a revised target state equation and to a time scaling law for the related uncertainty that in the long-term is shown to be orders of magnitude lower than under the nearly-constant velocity assumption. In support of the proposed model, an analysis of a significant portion of the real-world maritime traffic is provided.
This paper introduces multi-target filtering advances for challenging multi-target tracking scenarios. First, we propose an Interacting Multiple Model (IMM) filter for tracking evasive move-stop-move targets, by exploiting a modified Ornstein Uhlenbeck (OU) process model for target motion. Second, we introduce an asynchronous approach to data association that is applicable to multi-sensor settings where update rates and information content vary greatly across sensors. We validate improved performance using global nearest neighbor (GNN) data association and discuss its applicability to multi-target tracking (MTT) under the MHT paradigm.
Driven by real-world issues in maritime surveillance, we consider the problem of estimating the target state from a sequence of observations that can be imprecisely timestamped. That is, the time between two consecutive observations can be affected by an unknown error or delay. We propose an adaptive filtering strategy able to sequentially detect the time delays and correctly estimate the target state. Two decision statistics for the presence of delay are derived, the first is non-parametric while the second is based on the Generalized Likelihood Ratio Test (GLRT). When a delayed measurement is detected, the Maximum Likelihood (ML) estimate of the delay can be used to correct the timestamps of the target observation used in the filter. The validation of the proposed method is carried out using Monte Carlo computer simulations and analyzing real-world data collected by a global network of Automatic Identification System (AIS) receivers.
This paper presents a multi-target tracking data fusion network architecture based on multiple Wellen Radar (WERA) sensors for application in the maritime surveillance (MS) domain.
The WERA sensors are low-power HF surface-wave radars, developed for ocean remote sensing, but also capable of providing cost-effective long-range ship detection capabilities.
The system performance, investigated using the navigation reports from the automatic identification system as ground truth information, are discussed in two real study cases: The first in the Ligurian Sea, Mediterranean Sea, and the second currently ongoing in the German Bight, North Sea.
Here, the data recorded by the three stations are sent directly to the Science and Technology Organization (STO) Centre for Maritime Research and Experimentation (CMRE) database and then processed in real-time.
The historical information about ship traffic can be exploited not only for assessing system performance, but also in the field of knowledge-based tracking, for improving system capabilities.
In fact, the proposed architecture has to be intended not as standalone but as a component of a more complex MS system, exploiting different sensors, vessel navigation reports and other contextual information.
In this sense, not only the experimental results are presented and discussed, but also directions about current and future developments.
These last decades spawned a great interest towards low-power High-Frequency (HF) Surface-Wave (SW) radars for ocean remote sensing. By virtue of their over-the-horizon coverage capability and continuous-time mode of operation, these sensors are also effective long-range early-warning tools in maritime situational awareness applications providing an additional source of information for target detection and tracking.
Unfortunately, they also exhibit many shortcomings that need to be taken into account, and proper algorithms need to be exploited to overcome their limitations. In this paper, we develop a Knowledge-Based (KB) multitarget
tracking methodology, that takes advantage of a priori information on the ship traffic. This a priori information is
given by the ship sea lanes and by their related motion models, which together constitute the basic building blocks of a Variable Structure Interactive Multiple Model (VS-IMM) procedure. False alarms and missed detections are dealt with using a Joint Probabilistic Data Association (JPDA) rule and non-linearities are handled by the means of the Unscented Kalman Filter (UKF). The KB-tracking procedure is validated using real data acquired during an HF-radar experiment in the Ligurian Sea (Mediterranean Sea). Two HFSW radar systems were operated to develop and test target detection and tracking algorithms. The overall performance is defined in terms of time-on-target, false alarm rate, track fragmentation, and accuracy. A full statistical characterization is provided using one month of data. A significant improvement of the KB-tracking procedure, in terms of system performance, is demonstrated in comparison with the standard approach recently presented in [1]. The main improvement of our approach is the better capability of following targets without increasing the false alarm rate. This increment is much more evident in the region of low false alarm rate where it can be over the 30% for both the HFSW radar systems. The KB-tracking exhibits on average a reduction of the track fragmentation of about the 20% and the 13% of the utilized HFSW-radar systems.
This paper describes a fusion architecture that enables multi-level object tracking. The motivation is provided by the fusion of active and passive data, where multiple passive contacts may originate from the same platform at a given time. This poses a significant challenge to those target-tracking algorithms that are based on the assumption that each sensor provides at most one contact per target per scan or processing interval. For example, while each object has a unique radar return (except when considering extended objects with multiple reflectors), multiple passive contacts may arise when multiple emitter modes are onboard. Simulation results demonstrate the potential of the proposed architecture.
In the last decades, great interest has been directed towards low-power high-frequency (HF) surface-wave radars as long-range early-warning tools in maritime situational awareness applications. These sensors, developed for ocean remote sensing, provide an additional source of information for ship detection and tracking, by virtue of their over-the-horizon coverage capability and continuous-time mode of operation. Unfortunately, they exhibit many shortcomings that need to be taken into account, such as poor range and azimuth resolution, high non-linearity and significant presence of clutter.
In this paper, radar detection, multi-target tracking and data fusion techniques are applied to experimental data collected during an HF-radar experiment, which took place between May and December 2009 on the Ligurian coast of the Mediterranean Sea. The system performance is defined in terms of time-on-target, false alarm rate, track fragmentation and accuracy. A full statistical characterization is provided using one month of data. The effectiveness of the tracking and data fusion procedures is shown in comparison to the radar detection algorithm. In particular the detector’s false alarm rate is reduced by one order of magnitude. Improvements, using the data fusion of the two radars, are also reported in terms of time-on-target as well as accuracy.
Multi-target filtering for closely-spaced targets leads to degraded
performance with respect to single-target filtering solutions, due to
measurement provenance uncertainty. Soft data association approaches
like the probabilistic data association filter (PDAF) suffer track
coalescence. Conversely, hard data association approaches like
multiplehypothesis tracking (MHT) suffer track repulsion. We introduce
the stochastic data association filter (SDAF) that utilizes the PDAF
weights in a stochastic, hard data association update step. We find that
the SDAF outperforms the PDAF, though it does not match the performance
of the MHT solution. We compare as well to the recentlyintroduced
equivalence-class MHT (ECMHT) that successfully counters the track
repulsion effect. Simulation results are based on the steady-state form
of the Ornstein-Uhlenbeck process, allowing for lengthy stochastic
realizations with closely-spaced targets.
This paper analyzes a Mixed Ornstein-Uhlenbeck (MOU) process that has a number of appealing properties as a target motion model. Relevant earlier models that have been proposed include the standard nearly constant velocity motion model (unbounded long-term position and velocity), the Ornstein-Uhlenbeck process (bounded position, but no defined velocity), and the Integrated Ornstein-Uhlenbeck process (bounded velocity, but unbounded position). The Mixed Ornstein-Uhlenbeck (MOU) process exhibits drift terms in both position and velocity, and thus has a well-behaved limit for both. The initial target state can be defined in a natural way based on the steady-state characteristics of the MOU process, leading to a stationary stochastic process. Similarly, multi-target stationarity is achieved by choosing the initial target birth distribution according to the steady-state distribution on the number of targets. The MOU process can be used both in simulations and, correspondingly, in Kalman-based recursive filtering as part of multi-target tracking solutions.1 2
In this paper, we study the detailed distributional properties of integrated non-Gaussian Ornstein–Uhlenbeck (intOU) processes. Both exact and approximate results are given. We emphasize the study of the tail behaviour of the intOU process. Our results have many potential applications in financial economics, as OU processes are used as models of instantaneous variance in stochastic volatility (SV) models. In this case, an intOU process can be regarded as a model of integrated variance. Hence, the tail behaviour of the intOU process will determine the tail behaviour of returns generated by SV models.
High-frequency (HF) radars are operated in the 3-30 MHz frequency band and are known to cover ranges up to some thousand kilometers. Sky wave over-the-horizon radars (OTHR) utilize reflection by the ionosphere, but they require a transmit power up to 100 kilowatts. Especially for oceanographic applications, low power high frequency surface wave radar (HFSWR) systems have been developed, which use ground wave propagation along the salty ocean surface. The WERA HF radar system transmits a power as low as 30 watts, but achieves detection ranges up to 200 kilometers, which are far beyond the conventional microwave radar coverage. Due to external noise, radio frequency interference, and different kinds of clutter, special techniques for target detection have to be applied. This paper describes a new signal processing approach based on a curvilinear regression analysis for thresholding combined with a constant false-alarm-rate (CFAR) algorithm for detection. The target locations detected by the HF radar are passed to a tracking filter utilizing range, azimuth, as well as radial and azimuthal velocities to track the ship locations. For a 12-hour period real HF radar data from the WERA system were processed and secondary ship locations were recorded from the automatic identification system (AIS). This data set is used to assess the performance of the HF radar detections. Comparisons have been made for a maximum distance of 5 km between AIS and radar detected locations. The deviation between AIS and radar detected locations was below 1 kilometer in 77% of these comparisons. A number of ships was detected and tracked by the radar, but could not be used for comparisons due to the lack of AIS information.
Models of animal movement are necessary both as unambiguous descriptions of particular movement patterns and as starting points for the interpretation of observations on location. Radio-tracking, with frequent sampling so that successive observations are dependent, is an important special case to which only a single narrow class of models has been applied. The standard approach uses a bivariate Ornstein–Uhlenbeck diffusion process, for which the stationary distribution is always normal, limiting its flexibility for modelling stationary home range or territorial behaviour. I describe a new class of random diffusion models for animal movement, flexible enough to incorporate many realistic features, but simple enough to be estimated statistically and to be interpreted behaviourally. The models incorporate a finite number of different behavioural or physiological states for an animal, and a set of diffusion rules describing the movement of the animal while in particular states. Mathematically, the models are diffusions in finite random temporal environments. The class of models generalizes the standard Ornstein–Uhlenbeck model (which can be thought of as the 1-state case), and can represent features such as multimodal and asymmetric home ranges and utilization distributions. I give a range of examples of particular models within this class, including an application to the modelling of the movements of wood mice, for which radio-tracking can give information on behaviour, as well as location.
components of all the atoms of the system), and if the system of particles per unit volume is v, then in the case of v is
A numerical simulation algorithm that is exact for any time step Δt>0 is derived for the Ornstein-Uhlenbeck process X(t) and its time integral Y(t). The algorithm allows one to make efficient, unapproximated simulations of, for instance, the velocity and position components of a particle undergoing Brownian motion, and the electric current and transported charge in a simple R-L circuit, provided appropriate values are assigned to the Ornstein-Uhlenbeck relaxation time τ and diffusion constant c. A simple Taylor expansion in Δt of the exact simulation formulas shows how the first-order simulation formulas, which are implicit in the Langevin equation for X(t) and the defining equation for Y(t), are modified in second order. The exact simulation algorithm is used here to illustrate the zero-τ limit theorem. © 1996 The American Physical Society.
High-frequency (HF) radars are operated in the 3-30 MHz frequency range and need to share the frequency bands with other radio services. Due to their over-the-horizon (OTH) capabilities, HF radars play an important role in remote sensing and surveillance. The propagation conditions of the electromagnetic wave depend on the earth's ionosphere and mailnly follow a daily cycle. Communication paths between the HF radar and other radio services, some thousands of kilometres off, open and close with a high variability. Special care must be taken to dynamically adapt the HF radar's characteristics to the varying electromagnetic environment. The impact of a frequency modulated continuous wave (FMCW) HF radar on other radio services is not very strong, because of its low transmit power and utilisation of the radio spectrum. However, strong signals from other radio services can significantly reduce the performance of the oceanographic measurements. Several radar control and signal processing steps are discussed in this paper. All together form an effective procedure to reduce the impact of Radio Frequency Interference (RFI) on the oceanographic measurements.
In this paper, a novel (according to the authors' knowledge) type of scanning synthetic aperture radar (ScanSAR) that solves the problems of scalloping and azimuth-varying ambiguities is introduced. The technique employs a very simple counterrotation of the radar beam in the opposite direction to a SPOT: hence, the name terrain observation with progressive scan (TOPS). After a short summary of the characteristics of the ScanSAR technique and its problems, TOPSAR, which is the technique of design, the limits, and a focusing technique are introduced. A synthetic example based on a possible future system follows
This is the first part of a comprehensive and up-to-date survey of the techniques for tracking maneuvering targets without addressing the so-called measurement-origin uncertainty. It surveys various mathematical models of target motion/dynamics proposed for maneuvering target tracking, including 2D and 3D maneuver models as well as coordinate-uncoupled generic models for target motion. This survey emphasizes the underlying ideas and assumptions of the models. Interrelationships among models and insight to the pros and cons of models are provided. Some material presented here has not appeared elsewhere.
The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.
Tracking and Data Fusion: A Handbook of Algorithms
- Y Bar-Shalom
- P Willett
- X Tian
Y. Bar-Shalom, P. Willett, and X. Tian, Tracking and Data Fusion: A
Handbook of Algorithms. Storrs, CT: YBS Publishing, Apr. 2011.
Derivation of the Backscattering Coefficient
- H Laur
- P Bally
- P Meadows
- J Sanchez
- B Schaettler
- E Lopinto
- D Esteban
- Geosci
Geosci. Remote Sens., vol. 52, no. 8, pp. 5056-5071, Aug. 2014.