ArticlePublisher preview available

A tutorial on tomographic synthetic aperture radar methods

Authors:
Seyed Alireza Khoshnevis at University of South Florida
Seyed Alireza Khoshnevis
Seyed Ghorshi
Seyed Ghorshi
Seyed Ghorshi
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Read publisher preview
Download citation
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

Synthetic Aperture Radar (SAR) is a type of radar that is mounted on an airborne platform and aims to increase the resolution of the acquisitions by traveling over the target area. The signals acquired by SAR are two dimensional, but it is possible to create three dimensional models using signal processing methods. Tomography is the method of creating three dimensional models from multiple two dimensional signals. This method can be applied to SAR acquisitions to create three dimensional models of the landscape. The goal of this study is to create a comprehensive tutorial on how to work with SAR data, what toolbox to use for analysis, how to create a tomographic SAR dataset, and how to use different methods of spectral estimation for SAR tomography. In this work, first, we focus on the main problem of SAR tomography followed by SAR preprocessing steps, a brief description of data levels, and finally, we discuss the different spectral estimation methods used in SAR tomography along with examples at every step.
Figures - available from: SN Applied Sciences
This content is subject to copyright. Terms and conditions apply.
A preview of this full-text is provided by Springer Nature.
Learn more
Content available from SN Applied Sciences
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
SN Applied Sciences (2020) 2:1504 | https://doi.org/10.1007/s42452-020-03298-6
Review Paper
A tutorial ontomographic synthetic aperture radar methods
SeyedAlirezaKhoshnevis1 · SeyedGhorshi2
Received: 18 February 2020 / Accepted: 5 August 2020 / Published online: 12 August 2020
© Springer Nature Switzerland AG 2020
Abstract
Synthetic Aperture Radar (SAR) is a type of radar that is mounted on an airborne platform and aims to increase the
resolution of the acquisitions by traveling over the target area. The signals acquired by SAR are two dimensional, but it
is possible to create three dimensional models using signal processing methods. Tomography is the method of creat-
ing three dimensional models from multiple two dimensional signals. This method can be applied to SAR acquisitions
to create three dimensional models of the landscape. The goal of this study is to create a comprehensive tutorial on
how to work with SAR data, what toolbox to use for analysis, how to create a tomographic SAR dataset, and how to use
dierent methods of spectral estimation for SAR tomography. In this work, rst, we focus on the main problem of SAR
tomography followed by SAR preprocessing steps, a brief description of data levels, and nally, we discuss the dierent
spectral estimation methods used in SAR tomography along with examples at every step.
Keywords Synthetic aperture radar (SAR)· Comperssive sensing (CS)· Nonlinear least squares (NLS)· Tomographic SAR
(Tomo-SAR)· Interferometric SAR (In-SAR)· Singular value decomposition (SVD)
1 Introduction
In the late 1940s, after the second world war, the United
States army was looking for an all-weather, 24-h remote
surveillance device. The ability of the radar to penetrate
cloud and fog and its independence from daylight made
it the logical choice for the army. The only issue was that
in order to achieve a high enough resolution the antenna
would need to be the size of a football eld, far too large
for any aircraft to carry. Synthetic aperture radar (SAR) was
the solution to this problem. It was invented by Carl A.
Wiley, a mathematician at Goodyear Aircraft Company, in
1951. This technology was released to the civilian commu-
nities in the 1970s. This type of radar increases the resolu-
tion by using the movement of the platform to create a
synthetic aperture.
SAR stores the data of the scanned area in the form
of a 2-dimensional signal (similar to an image); however,
the actual landscape is 3-dimensional. Therefore, SAR in
fact maps the information of the 3-dimensional area into
two dimensions. During this mapping process, not only
we lose the data for the third dimension, but also since
the model is summed in one direction, it becomes less
accurate. The process of tomography aims to untangle this
mapped version of the landscape and estimate the origi-
nal 3-dimensional model by calculating the reectivity and
elevation of the scatterers using multiple SAR acquisitions.
Any object or surface that comes into contact with the
beam scatters the signal in all direction which is why we
use the term “scatterer” to describe them. The goal of this
study is to create a tutorial for the SAR tomography (tomo-
SAR) process using real SAR data. To this end, in the sec-
ond section, we discuss what SAR is and introduce some
of the most famous platforms on which SAR equipment
are mounted. The third section covers what tomo-SAR is
and the mathematical modeling and equations support-
ing this method. The fourth section includes all the pre-
processing steps required for SAR data processing and the
* Seyed Alireza Khoshnevis, khoshnevis@usf.edu; Seyed Ghorshi, aghorshi@uttyler.edu | 1Department ofElectrical Engineering,
University ofSouth Florida, Tampa, FL, USA. 2Department ofElectrical Engineering, The University ofTexas atTyler, Tyler, TX, USA.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... This enables the segregation of scattering signals from different heights and their projection in the same range/azimuth resolution cell. The third dimension can be estimated more accurately by taking multiple parallel acquisitions of a single area, eliminating shadowing or layover phenomena [13,14]. In 2021, Minh et al. proposed a method for classifying different types of forests based on Pband tomographic SAR data obtained by using the F-SAR sensor of the German Aerospace Center [15]. ...
Ant Nest Detection Using Underground P-Band TomoSAR
Preprint
Full-text available
  • Dec 2024
Leaf-cutting ants, notorious for causing defoliation in commercial forest plantations, significantly contribute to biomass and productivity losses, impacting forest producers in Brazil. These ants construct complex underground nests, highlighting the need for advanced monitoring tools to extract subsurface information across large areas. Synthetic Aperture Radar (SAR) systems provide a powerful solution for this challenge. This study presents the results of electromagnetic simulations designed to detect leaf-cutting ant nests in industrial forests. The simulations modeled nests with 6 to 100 underground chambers, offering insights into their radar signatures. Following these simulations, a field study was conducted using a drone-borne SAR operating in the P-band. A helical flight pattern was employed to generate high-resolution ground tomography of a commercial eucalyptus forest. A convolutional neural network (CNN) was implemented to detect ant nests and estimate their sizes from tomographic data, delivering remarkable results. The method achieved an ant nest detection accuracy of 100%, a false alarm rate of 0%, and an average error of 21% in size estimation. These outcomes highlight the transformative potential of integrating Synthetic Aperture Radar (SAR) systems with machine learning to enhance monitoring and management practices in commercial forestry.
View
... TomoSAR techniques have expanded interferometric methods to enable multidimensional imaging. By utilizing multiple SAR images captured from various angles, it allows for 3D imaging, such as mapping urban areas [50,51]. In this paper, resolution outcomes are examined rather than explaining imaging modes and advanced techniques. ...
Spaceborne SAR System Design Considerations: Minimizing Satellite Size and Mass, System Parameter Trade-Offs, and Optimization
Article
Full-text available
  • Oct 2024
The goal of this research is to assess and guide the development of next-generation synthetic aperture radar (SAR) satellites, optimize their performance, and present the requirements related to the design parameters. In the current era, characterized by the rapid advancement of SAR technologies, the challenge of designing compact and efficient satellites is more relevant than ever. The present research provides a comprehensive analysis of design parameters for microsatellite applications, including altitude, incidence angle, operating frequency, antenna sizing, and transmitting power. The complex relationships between these parameters and their combined impact on SAR system performance and satellite dimensions are demonstrated through various scenarios. Special attention is given to balancing the SAR antenna area and the transmitting power needs, which are primary constraints in SAR microsatellite design. A detailed comparative study is presented, highlighting how each design decision affects the overall functionality and performance. Modern SAR satellites with masses under 150 kg can operate with approximately 1 kW of transmitting power and a 10 m2 SAR antenna area. The present results analyze and validate the key parameters related to these satellites, coping with the challenging trade-offs through optimization. Furthermore, this study aims to guide future innovative spaceborne SAR system design, highlighting the potential of optimization techniques in advancing spaceborne SAR technology.
View
... SAR acquisition modeKhoshnevis and Ghorshi (2020). ...
Using InSAR Techniques to Estimate the Ground Deformation Resulting from Earthquakes in Northeast Iraq
Thesis
Full-text available
  • Sep 2023
To comprehensively comprehend the potential risks posed by seismic activity and the formation of geological structures in areas with high tectonic activity, it is critical to conduct thorough investigations on the deformation of the Earth's crust caused by earthquakes. By analyzing the changes in the crust's shape and movements resulting from seismic events, researchers can gain valuable information on the underlying geological processes and the potential for future earthquakes. This knowledge can help in developing effective strategies to mitigate the impact of earthquakes on society and infrastructure, as well as contribute to a deeper understanding of the Earth's dynamic systems. In this study, we use Sentinel-1 interferometric synthetic aperture radar (InSAR) images, extending across a combination of two tracks that ascending and two tracks that descending. Two well-known processing methods, LiCSAR and DinSAR, were utilized to create the seismic deformation field and produce a time series of 2D deformation. One of the most powerful earthquakes to strike the region since 1900 happened on November 12, 2017, in Sarpol Zahab City located at the Iraq-Iran border. Another series of seismic events occurred approximately 100 kilometers to the south, near Mandali-Sumar in Iraq, on January 11, 2018. These earthquakes were noteworthy due to their intensity and proximity to populated areas, highlighting the potential risks and dangers associated with seismic activity in the region. This paper aims to create a map of neotectonics deformation of the ground along the Iraq-Iran borders from 2014 to 2023 and detect fault activity because of the earthquakes that occurred on November 12 and January 11, 2018, using inSAR techniques and confirmation on the processing, dissection, and applications of Sentinel-1 data backed by field, and tectonic information. The time series of 2D seismic deformation demonstrated a gradual decreasing pattern, which corresponded well with the distribution of aftershocks. The results show that the deformation for the Sarpol Zahab region extends to an area of 70 km2 and 90 km2, the greatest line-of-sight displacement of horizontal deformation is 20 cm and vertical displacement is 100 cm uplifting and 35 cm subsidence. The results of deformation for the Mandali-Sumar region extend to an area of 27 km2 and 23 km2, the greatest line of sight displacement of horizontal deformation is 5 cm and vertical displacement is 20 cm uplifting and 5 cm subsidence for ascending and descending data, respectively. The outcomes of this research will provide valuable insights into the investigation of tectonic events occurring in the region.
View
... Synthetic Aperture Radar (SAR) is a remote sensing technology that uses microwave signals to observe the Earth's surface. Unlike optical remote sensing techniques, SAR has the advantage of being able to operate in all weather conditions and at all times of day [1]. SAR images are widely used in various fields due to their unique advantages. ...
Zero-Shot SAR Target Recognition Based on a Conditional Generative Network with Category Features from Simulated Images
Article
Full-text available
  • May 2024
SAR image target recognition relies heavily on a large number of annotated samples, making it difficult to classify the unseen class targets. Due to the lack of effective category auxiliary information, the current zero-shot target recognition methods for SAR images are limited to inferring only one unseen class rather than classifying multiple unseen classes. To address this issue, a conditional generative network with the category features from the simulated images for zero-shot SAR target recognition is proposed in this paper. Firstly, the deep features are extracted from the simulated images and fused into the category features that characterize the entire class. Then, a conditional VAE-GAN network is constructed to generate the feature instances of the unseen classes. The high-level semantic information shared in the category features aids in generalizing the mapping learned from the seen classes to the unseen classes. Finally, the generated features of the unseen classes are used to train a classifier that can classify the real unseen images. The classification accuracies for the targets of the three unseen classes based on the proposed method can reach 99.80 ± 1.22% and 71.57 ± 2.28% with the SAMPLE dataset and the MSTAR dataset, respectively. The advantage and validity of the proposed architecture are indicated with a small number of the seen classes and a small amount of the training data. Furthermore, the proposed method can be extended to generalized zero-shot recognition.
View
... These sharp reflections are best suited to reconstruct three-dimensional objects like buildings. The processed SAR data is similar to an image that a hypothetical radar camera would take when pointed at earth's surface [KG20]. The goal of SAR tomography is to reconstruct 3D information from parts, or even single pixels of the two-dimensional "image". ...
The Quantum Fourier Transform for Earth Observation
Thesis
  • Oct 2022
The Fourier transform algorithm is ubiquitously used and essential for applications like digital signal processing, as well as audio and video compression. Its quantum analogue, the quantum Fourier transform (QFT), first became famous as a part of Shor’s Algorithm for factoring numbers in the nineties. Since then, many influential algorithms have been built on top of the QFT and the associated quantum phase estimation. One of these is a quantum algorithm for solving systems of linear equations. It was developed by Harrow, Hassidim and Loyd in 2009 [HHL09] and is hence often called HHL for short. Under certain conditions, HHL is able to find the solution x of the system Ax = b with a complexity of only O(log n), where n is the number of variables. This stands in contrast to classical techniques which achieve a runtime of O(n 2) at best. In this work, we develop the HHL algorithm and the components upon which it is built, including the quantum Fourier transform. While the theoretical speedup of HHL is exponential, the conditions which must be met can pose difficult problems for actual implementations. We give an in-depth overview of these conditions and the research aimed to improve them. At DLR large amounts of earth observation data from satellites needs to be processed. This presents a possible future use case for quantum algorithms. We evaluate the applicability of HHL for space-borne synthetic aperture radar tomography, which is a technique to reconstruct three-dimensional surface features from radar data.
View
View
Atomic Norm Minimization Based Fast Off-Grid Tomographic SAR Imaging With Nonuniform Sampling
Article
  • Jan 2024
The accuracy of the traditional compressed sensing (CS) based tomographic synthetic aperture radar (TomoSAR) imaging is limited by the inappropriate grid partitioning. The atomic norm based processing effectively solves this problem by implementing variable estimation in the continuous domain, that is, avoiding the undesired grid partitioning manipulation. Nevertheless, the performance of the atomic norm based TomoSAR imaging is limited in two main aspects: limited geometry adaptability caused by the uniform sampling requirement and the high computational load. In this paper, a novel atomic norm minimization (ANM) based off-grid TomoSAR imaging is proposed for the fast processing with nonuniform sampling. The main technical contributions are twofold: First, the nonuniformly sampled data is resampled to be uniform where a new geometrical projection-based interpolation is used; Second, the ANM problem is solved by using the non-symmetric cone model to speed up the processing, reducing the computational load from O ( N 2 ) to O ( N ). The proposed approaches have been verified by the computer simulations and the real data experiments.
View
Method for Estimating Targets’ Dimensions Using Aerial Surveillance Cameras
Article
  • Dec 2023
Determining the size of objects (static or moving) appearing in the aerial imagery can foster targets’ reconnaissance and support rapid decision-making during the flight. This paper presents a novel method, based on previous consolidated approaches, to measure the dimensions of any target appearing in an aerial image, either vertically (height), horizontally (length, width), or even areas. It works with optical cameras; no other payload sensors are needed. It requires a single optical image and a short processing time (it can work in real-time during the flight). The required parameters for the calculation are the focal length, detector dimensions, Pitch and Roll (Euler) angles, and the relative elevation of the aerial camera from the points where the targets are. This latter parameter is strongly dependent on the positional accuracy of the aerial system, which is not always very high. Therefore, this paper also proposes an approach to retrieve the accurate elevation by considering only two points of known coordinates visible in the image. The coordinates of these points can be obtained using Digital Elevation Models (DEMs), which can subsequently allow retrieving the relative elevation of the camera at any other point of the image. The method was tested using an accurate DEM and 24 aerial images acquired by a drone. The results showed that in most of the cases considered, the metrics were estimated with an error lower than 5 cm. In the last part of the paper, potential limitations and solutions to automatize the processes were discussed.
View
Application of space-based SAR in single-pass squint mode in Earth tomographic study
Article
Full-text available
  • May 2023
Construction features and main characteristics of a tomographic complex based on a single-position space radar with synthetic aperture radar (SAR) operating in the single-pass squint Earth observation mode are considered. The operating conditions and requirements for information support of the multichannel signal processing system are determined. The operability of the proposed approach using real signals of two wavelength ranges obtained during experimental flights was tested. The tomographic SAR (TSAR) under consideration makes it possible to perform a rapid four-dimensional assessment during one pass of the carrier, without using a second receiving antenna. The algorithmic implementation of paired signals multidimensional processing is described, the verification and debugging of which was carried out using real radio holograms. The feature of this TSAR construction is the possibility to use one increased synthesis interval, with a division into sub-intervals to enable spatial diversity of multi-pair receiving apertures, in the implementation of multi-dimensional phase-difference radar measurements. The possibility demonstrated to use the proposed construction design of the tomographic complex with SAR in solving tasks related to obtaining additional information on the state of the Earth’s surface. This paper presents the first experimental results of imaging the layered inhomogeneities of the underlying surface by using multidimensional phase-difference processing of paired signals from a single-pass SAR operating in squint mode.
View
UAV-Based P-Band SAR Tomography with Long Baseline: A Multi-Master Approach
Article
  • Jan 2023
Due to the advantage of flexible and rapid deployment, unmanned aerial vehicle (UAV) based synthetic aperture radar (SAR) tomography (TomoSAR) is a promising technology in three-dimensional (3D) urban mapping.The long baseline is indispensable for P-band SAR systems to achieve high elevation resolution. It will introduce two problems.On the one hand, the unavoidable spatial decorrelation brings serious phase noise and sidelobes in 3D imaging.On the other hand, the noticeable image distortion fails the image registration and the TomoSAR data stack (TDS) construction. Aiming at the above problems, this paper proposes a multi-master (MM) TomoSAR approach via three main contributions. First, the traditional TomoSAR signal model is extended to the MM case to improve the number of baselines and the average image coherence of the TDS, and suppress the sidelobes. Second, a short-baseline-recursion image registration method is proposed to achieve high-precision image registration. Third, a TDS optimization processing consisting of interferometric SAR (InSAR) phase screening and baseline sign reassignment is introduced. Moreover, a clustering-based outliers elimination method is also adopted to ensure the 3D imaging quality. Computer simulation and long-baseline P-band UAV-SAR experiment validate the proposed approach.
View
SAR Tomography at the Limit: Building Height Reconstruction Using Only 3-5 TanDEM-X Bistatic Interferograms
Article
Full-text available
  • May 2020
Multibaseline interferometric synthetic aperture radar (InSAR) techniques are effective approaches for retrieving the 3-D information of urban areas. In order to obtain a plausible reconstruction, it is necessary to use more than 20 interferograms. Hence, these methods are commonly not appropriate for large-scale 3-D urban mapping using TanDEM-X data, where only a few acquisitions are available in average for each city. This article proposes a new SAR tomographic processing framework to work with those extremely small stacks, which integrates the nonlocal filtering into SAR tomography inversion. The applicability of the algorithm is demonstrated using a TanDEM-X multibaseline stack with five bistatic interferograms over the whole city of Munich, Germany. A systematic comparison of our result with TanDEM-X raw digital elevation models (DEMs) and airborne LiDAR data shows that the relative height accuracy of two-third buildings is within 2 m, which outperforms the TanDEM-X raw DEM. The promising performance of the proposed algorithm paved the first step toward high-quality large-scale 3-D urban mapping.
View
Monitoring Tropical Forest Structure Using SAR Tomography at L-and P-Band
Article
Full-text available
  • Aug 2019
Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior~to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band's performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities.
View
remote sensing Monitoring Tropical Forest Structure Using SAR Tomography at L-and P-Band
Article
Full-text available
  • Aug 2019
Our study aims to provide a comparison of the P- and L-band TomoSAR profiles, Land Vegetation and Ice Sensor (LVIS), and discrete return LiDAR to assess the ability for TomoSAR to monitor and estimate the tropical forest structure parameters for enhanced forest management and to support biomass missions. The comparison relies on the unique UAVSAR Jet propulsion Laboratory (JPL)/NASA L-band data, P-band data acquired by ONERA airborne system (SETHI), Small Footprint LiDAR (SFL), and NASA Land, Vegetation and Ice Sensor (LVIS) LiDAR datasets acquired in 2015 and 2016 in the frame of the AfriSAR campaign. Prior to multi-baseline data processing, a phase residual correction methodology based on phase calibration via phase center double localization has been implemented to improve the phase measurements and compensate for the phase perturbations, and disturbances originated from uncertainties in allocating flight trajectories. First, the vertical structure was estimated from L- and P-band corrected Tomography SAR data measurements, then compared with the canopy height model from SFL data. After that, the SAR and LiDAR three-dimensional (3D) datasets are compared and discussed at a qualitative basis at the region of interest. The L- and P-band’s performance for canopy penetration was assessed to determine the underlying ground locations. Additionally, the 3D records for each configuration were compared with their ability to derive forest vertical structure. Finally, the vertical structure extracted from the 3D radar reflectivity from L- and P-band are compared with SFL data, resulting in a root mean square error of 3.02 m and 3.68 m, where the coefficient of determination shows a value of 0.95 and 0.93 for P- and L-band, respectively. The results demonstrate that TomoSAR holds promise for a scientific basis in forest management activities.
View
DSCC2019-9042 DETERMINATION OF ROLES AND INTERACTION MODES IN A HAPTIC SHARED CONTROL FRAMEWORK
Conference Paper
Full-text available
  • Jul 2019
Successful collaboration requires the integration of the individual partners' intentions into a shared action plan, which may involve a continuous negotiation of intentions and roles. Negotiation of roles, if handled effectively, could prevent bumpy transitions of control authority between the human and robot. Lacking for Haptic Shared Control, however, is a means to identify the roles and means to monitor or measure the interaction mode between the human and a collaborative robot. This paper aims to address this oversight by employing new measures and associated analysis. Specifically, we use relative force measurement to identify the interaction mode (cooperative and non-cooperative). Furthermore, we use work as a tool to determine the roles of the human partner and collaborative robot in each of the interaction modes.
View
Introducing Spatial Regularization in SAR Tomography Reconstruction
Article
Full-text available
  • Jan 2018
The resolution achieved by current synthetic aperture radar (SAR) sensors provides a detailed visualization of urban areas. Spaceborne sensors such as TerraSAR-X can be used to analyze large areas at a very high resolution. In addition, repeated passes of the satellite give access to temporal and interferometric information on the scene. Because of the complex 3-D structure of urban surfaces, scatterers located at different heights (ground, building facade, and roof) produce radar echoes that often get mixed within the same radar cells. These echoes must be numerically unmixed in order to get a fine understanding of the radar images. This unmixing is at the core of SAR tomography. SAR tomography reconstruction is generally performed in two steps: 1) reconstruction of the so-called tomogram by vertical focusing, at each radar resolution cell, to extract the complex amplitudes (a 1-D processing) and 2) transformation from radar geometry to ground geometry and extraction of significant scatterers. We propose to perform the tomographic inversion directly in ground geometry in order to enforce spatial regularity in 3-D space. This inversion requires solving a large-scale nonconvex optimization problem. We describe an iterative method based on variable splitting and the augmented Lagrangian technique. Spatial regularizations can easily be included in this generic scheme. We illustrate, on simulated data and a TerraSAR-X tomographic data set, the potential of this approach to produce 3-D reconstructions of urban surfaces.
View
View
View
Investigation of Kronecker-Based Recovery of Compressed ECG Signal
Article
  • Aug 2019
Continuous measurement of Electrocardiogram (ECG) signal is required for detecting various cardiac abnormalities, such as arrhythmia. Wearable devices have become ubiquitous as continuous monitoring devices. Due to power and memory restrictions in wearable devices, signal acquisition may need to be done in smaller segments using compressive sensing (CS) techniques. However such acquisitions may lead to poor recovery of compressed measurements. A Kronecker-based novel recovery technique has been recently proposed to improve the recovery of compressed signals where the recovery is achieved through a single recovery of concatenated compressed signal segments. In this paper, a mathematical reasoning for improvement using the Kronecker-based recovery over standard CS recovery procedure is presented. A detailed investigation of Kroneckerbased recovery technique of compressed ECG signal is presented using ECG signals from MIT-BIH Arrhythmia database. As a part of this investigation, quality of recovery with random and deterministic sensing of ECG signals and impact of choice of sparsifying dictionaries under various compression ratios (CR) are considered. Deterministic sensing with deterministic binary block diagonal (DBBD) matrix and Discrete Cosine Transform (DCT) as sparsifying basis is seen to provide the best recovery for ECG signals. Kronecker-based recovery of noisy ECG signals is possible with DBBD measurement matrix.
View
Digital Terrain Model Retrieval in Tropical Forests Through P-Band SAR Tomography
Article
  • Apr 2019
This paper focuses on the retrieval of terrain topography below dense tropical forests by means of synthetic aperture radar (SAR) systems. Low-frequency signals are needed to penetrate such a thick vegetation layer; however, this expedient alone does not guarantee proper retrieval. It is, here, demonstrated that the phase center of P-band backscatter may lie several meters above the ground, depending on the slope and incidence angle. SAR tomography is shown to overcome this problem and retrieves the actual topography even in the presence of dense trees up to 50 m tall. Digital terrain models returned by SAR tomography are, here, put in comparison with light detection and ranging (LiDAR) terrain models: the accuracy of radar-derived maps is found to be at least comparable with the one offered by LiDAR systems. Moreover, the discrepancy between tomography and LiDAR is larger if large-footprint LiDAR is considered thus suggesting that, in this case, tomographic maps should be considered the reference height. Analyses are carried out by processing three data sets gathered over different tropical forests in western Africa. The robustness of the radar estimates is assessed with respect to both ground slope and treetop height.
View
Forest Analysis Using SAR Tomography and Maximum Likelihood Inspired Spectral Estimation
Conference Paper
  • Jun 2018
This paper treats the synthetic aperture radar (SAR) tomography (TomoSAR) non-linear inverse problem, within the framework of maximum likelihood (ML) estimation theory. In this context, a novel non-parametric spectral analysis (SA) technique, addressed in a closed fixed-point iterative fashion, is presented. The main goal of the proposed approach is to provide resolution-enhancement, with suppression of artifacts and ambiguity levels re-duction, to an initial estimate of the continuous power spectrum pattern (PSP), retrieved using the celebrated matched spatial filter (MSF) beamforming technique. The feature enhancing capabilities of the proposed method are corroborated via processing L-band airborne multi-baseline SAR data of the German Aerospace Center (DLR), acquired by the F-SAR system over the forested test site of Froschham, Germany, in 2017.
View