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The multifunction radar, aided by advances in electronically steered phased array technology, is capable
of supporting numerous, differing and potentially conflicting tasks. However, the full potential of the
radar system is only realised through its ability to automatically manage and configure the finite resource
it has available. This thesis det...
Citations
... Driven by advanced electronic technologies, such as digital phased array systems, radar is gradually evolving toward multifunctionality. For example, it has the ability to quickly change the waveform between pulses and can adaptively adjust the working state according to the task and external environment [3][4][5][6][7]. ...
... This paper established a radar state transition diagram using six operation modes of a MFR as states, as shown in Figure 12. The states 1 6 S S represent the STT, MTT, TAS, TWS, RWS, and VS modes, with their threat levels defined as 10,8,6,5,3, and 1, respectively. The VS mode had the lowest threat level and was set as the radar's target state. ...
With the advancement of radar technology toward multifunctionality and cognitive capabilities, traditional radar countermeasures are no longer sufficient to meet the demands of countering the advanced multifunctional radar (MFR) systems. Rapid and accurate generation of the optimal jamming strategy is one of the key technologies for efficiently completing radar countermeasures. To enhance the efficiency and accuracy of jamming policy generation, an efficient jamming policy generation method based on multi-timescale ensemble Q-learning (MTEQL) is proposed in this paper. First, the task of generating jamming strategies is framed as a Markov decision process (MDP) by constructing a countermeasure scenario between the jammer and radar, while analyzing the principle radar operation mode transitions. Then, multiple structure-dependent Markov environments are created based on the real-world adversarial interactions between jammers and radars. Q-learning algorithms are executed concurrently in these environments, and their results are merged through an adaptive weighting mechanism that utilizes the Jensen–Shannon divergence (JSD). Ultimately, a low-complexity and near-optimal jamming policy is derived. Simulation results indicate that the proposed method has superior jamming policy generation performance compared with the Q-learning algorithm, in terms of the short jamming decision-making time and low average strategy error rate.
... In this respect, the RRM can use different optimization tools to perform resource allocation. Among them, it is worth mentioning the quality of service resource allocation method (Q-RAM) [4] and the continuous double auction parameter selection (CDAPS) [5], [6]. ...
Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and reflective intelligent surfaces (RIS)-aided non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach.
... In detail, that means selecting random pairs i, j ∈ N of task indexes until the given number of substitutabilities is achieved and setting I ij = −1. Additionally, we need to define the substitution utility for task T i achieved by executing task T j defined by u i q ij (r j , e) required in (18). Since the tasks are modeled abstract, we assume that u i q ij (r j , e) is calculated by scaling the utility of task T j linearly using a randomly selected factor I s ij while ensuring a maximum utility of 1, i.e. u i q ij (r j , e) := min I s ij u j q j (r j , e) , 1 . ...
... In contrast to the previous section, in this section we calculate the terms u i q i (r i , e) required in (19) as well as u i q ij (r j , e) required in (18) using the tracking performance model described in the following. The tracking performance model is very similar to the one presented in [34]. ...
This paper presents an exact quality of service radar resource management model which considers pairwise dependencies between tasks and is based on mixed-integer programming. This is in contrast to the common approach, which is not able to model any dependencies and hence has to assume independence among the tasks, which is generally an incorrect simplification. We define and implement substitutabilities between radar tasks with regard to their utility. Numerical results demonstrate that in scenarios with substituabilities, the proposed method can achieve a significantly better operational radar performance than a traditional approach, especially in high load situations. Though, a drawback of the added complexity due to considering dependencies is an increased computational runtime in comparison to the common method. Due to implementing dependencies, the method is especially well suited for complex scenarios with many substituabilities among radar tasks that need to be exploited under high task load.
... 3) Adaptive revisit time based on NEES: the accuracy of the covariance is measured through the normalized estimation error squared (NEES) that, for a single sample in the case of constant velocity estimate, should be chi-square distributed with six degrees of freedom [36], [38]. Hence, the average value for the NEES should be six, where the confidence interval for the chi-square distribution is [ξ 2 L , ξ 2 H ]. For instance, the interval [0.87, 16.81] is derived setting the confidence to 98%. As a consequence, the revisit time can be set as the maximum revisit such that the estimated NEES is within a specific interval [ξ 2 L , ξ 2 H ]. It is finally, worth noticing that for all the above mentioned strategies the tuning parameters (viz. ...
The modern battlefield scenario is strongly influenced by the innovative capabilities of the multifunction phased array radars (MPARs) which can perform sequentially or in parallel a plethora of sensing and communication activities. As a matter of fact, the MPAR can functionally cluster its phased array into bespoke sub-apertures implementing different tasks. Accordingly, a portion of the other available resources, e.g., bandwidth, power-aperture product (PAP), and time, is also assigned to each sub-aperture and the grand challenge is the definition of strategies for an optimal scheduling of the tasks to be executed. In this respect, a rule-based algorithm for task scheduling is proposed in this paper. In a nutshell, in each time window, the procedure first allocates the radar tasks (viz. volume search, cued search, update and confirmation tracking) and then utilize the communication (COM) looks so as to fill the empty intra-slot time left by the radar tasks. When there are two concurrent looks, the allocation is performed according to their priorities. Moreover, if the bandwidth and PAP are sufficient, some of them can be also scheduled in parallel. Interesting results in term of bandwidth and time occupancy efficiency are observed from simulations conducted in challenging scenarios comprising also multiple maneuvering targets.
... In this respect, the RRM can use different optimization tools to perform resource allocation. Among them, it is worth mentioning the quality of service resource allocation method (Q-RAM) [4] and the continuous double auction parameter selection (CDAPS) [5], [6]. ...
Multifunction phased array radars (MPARs) exploit the intrinsic flexibility of their active electronically steered array (ESA) to perform, at the same time, a multitude of operations, such as search, tracking, fire control, classification, and communications. This paper aims at addressing the MPAR resource allocation so as to satisfy the quality of service (QoS) demanded by both line of sight (LOS) and non line of sight (NLOS) search operations along with communications tasks. To this end, the ranges at which the cumulative detection probability and the channel capacity per bandwidth reach a desired value are introduced as task quality metrics for the search and communication functions, respectively. Then, to quantify the satisfaction level of each task, for each of them a bespoke utility function is defined to map the associated quality metric into the corresponding perceived utility. Hence, assigning different priority weights to each task, the resource allocation problem, in terms of radar power aperture (PAP) specification, is formulated as a constrained optimization problem whose solution optimizes the global radar QoS. Several simulations are conducted in scenarios of practical interest to prove the effectiveness of the approach.
... It is well known that it is impossible for the jammer to fully understand all the parameters of the radar during the countermeasure. The jamming style and jamming strategy adopted by the jammer are only dependent on the results of its reconnaissance of the target radar [79], [80], [81], [82], [83]. ...
With the increasingly complex electromagnetic environment and the intelligent development of radar, the jammer, as opposed to radar, urgently needs to improve its ability to recognize threat targets and make jamming decisions. In this paper, we first introduce the concepts and systems of cognitive electronic warfare (CEW) and summarize its research status. Through analysis of the existing CEW systems, we propose a CEW model suitable for the cluster confrontation scenarios. Then, for the radar jamming decision-making (RJDM) namely a crucial part of CEW, we discuss the advantages, disadvantages, and applications of the traditional methods and analyze the machine-learning based methods including Markov decision processing, the newest Q-Learning, Deep Q-Learning (DQN), Double Deep Q-Learning (DDQN), A3C algorithms, and their improved algorithms etc. We build radar adversarial models and verify the effectiveness of reinforcement learning (RL) algorithm and the superiority of deep RL by simulating both the underlying Q-Learning and DQN algorithms. Finally, the research trends of CEW are discussed.
... In this section we will briefly describe the approximative solution strategy to the Q-RAM problem proposed in [1], [2]. A notable alternative approach is given in [6]. First, all possible task configurations are generated and evaluated on a per task basis, i.e. their resulting utility and resource requirement are computed. ...
An intelligent radar resource management is an essential building block of any modern radar system. The quality of service based resource allocation model (Q-RAM) provides a framework for profound and quantifiable decision-making but lacks a representation of inter-task dependencies that can e.g. arise for tracking and synchronisation tasks. As a consequence, synchronisation is usually performed in fixed non-optimal patterns. We present an extension of Q-RAM which enables the resource allocation to consider complex inter-task dependencies and can produce adaptive and intelligent synchronisation schemes. The provided experimental results demonstrate a significant improvement over traditional strategies.
... For now on, the utility function we use is decreasing with distance to guarantee a good signal-to-noise ratio (SNR). The choices of the utility function and of the policy of selection can also add priority mechanisms, fuzzy logic or other advanced concepts such as those mentioned in [7]. We use mechanical steering in our work; the utility and policy can be adapted to harness the assets of electronic beamsteering, such as the ability to dynamically switch between different areas. ...
Building a labeled database usually requires extensive work when the labeling is not automated. This paper presents a methodology to build a labeled database of aircraft radar signature (specifically radar cross section) using the OpenSky Network data. This method relies on a radar system steered by the live API to automatically select, track and measure target aircraft according to custom user rules. The OpenSky ADS-B database allows for an efficient processing of the radar data, as it provides ranging and speed information to locate the region of interest inside the radar data. In addition, the ADS-B and Mode-S databases give an estimate of the attitude of the aircraft, which is paramount for signature measurement. We also compare the ADS-B data with the radar data.
... Towards this end, the continuous double auction parameter selection algorithm is proposed in refs. [7,120] which enables the solution from the previous time step to be adapted to the current time step without a complete re-computation of the resource allocation, hence reducing computation for dynamic RRM problem. ...
A multi‐function radar is designed to perform disparate functions, such as surveillance, tracking, fire control, amongst others, within a limited resource (time, frequency, and energy) budget. A radar resource management (RRM) module within a radar system makes decisions on prioritisation, parameter selection, and scheduling of associated tasks. However, optimal RRM algorithms are generally computationally complex and operational radars resort to heuristics. On the other hand, algorithms based on artificial intelligence (AI) have been shown to yield near‐optimal radar resource allocation results at manageable computational complexity. This survey study aims at enabling researchers and practitioners better understand the application of AI in RRM‐related problems by providing a thorough literature review of AI‐based RRM techniques. We first provide background concepts in RRM followed by a brief review of Symbolic‐AI techniques for RRM. We mainly focus on the applications of state‐of‐the‐art machine learning techniques to RRM. We emphasise on the recent findings and their potential within practical RRM scenarios for real‐time resource allocation optimisation. The study concludes with a discussion of open research problems and future research directions in the light of the presented survey.
... In this section we will briefly describe the approximative solution strategy to the Q-RAM problem proposed in [1], [2]. A notable alternative approach is given in [4]. First, all possible task configurations are generated and evaluated on a per task basis, i.e. their resulting utility and resource requirement are computed. ...
An intelligent radar resource management is an essential building block of any modern radar system. The quality of service based resource allocation model (Q-RAM) provides a framework for profound and quantifiable decision making but lacks the flexibility necessary for optimal mitigation strategies in the presence of interference. We define an extension of the Q-RAM based radar resource management framework with an intelligent interference handling capability using various mitigation methods. The approach incorporates virtual time resources and alternative task configurations to compute near-optimal solutions in the presence of interference. The provided experimental results demonstrate a significant improvement over traditional strategies.
