Motion Estimation and Compensation in Automotive MIMO SAR

With the advent of self-driving vehicles, autonomous driving systems will have to rely on a vast number of heterogeneous sensors to perform dynamic perception of the surrounding environment. Synthetic Aperture Radar (SAR) systems increase the resolution of conventional mass-market radars by exploiting the vehicle's ego-motion, requiring a very accurate knowledge of the trajectory, usually not compatible with automotive-grade navigation systems. In this regard, this paper deals with the analysis, estimation and compensation of trajectory estimation errors in automotive SAR systems, proposing a complete residual motion estimation and compensation workflow. We start by defining the geometry of the acquisition and the basic processing steps of Multiple-Input Multiple-Output (MIMO) SAR systems. Then, we analytically derive the effects of typical motion errors in automotive SAR imaging. Based on the derived models, the procedure is detailed, outlining the guidelines for its practical implementation. We show the effectiveness of the proposed technique by means of experimental data gathered by a 77 GHz radar mounted in a forward looking configuration.Comment: 14 page

Sparsity driven ground moving target indication in synthetic aperture radar

Synthetic aperture radar (SAR) was first invented in the early 1950s as the remote surveillance instruments to produce high resolution 2D images of the illuminated scene with weather-independent, day-or-night performance. Compared to the Real Aperture Radar (RAR), SAR is synthesising a large virtual aperture by moving a small antenna along the platform path. Typical SAR imaging systems are designed with the basic assumption of a static scene, and moving targets are widely known to induce displacements and defocusing in the formed images. While the capabilities of detection, states estimation and imaging for moving targets with SAR are highly desired in both civilian and military applications, the Ground Moving Target Indication (GMTI) techniques can be integrated into SAR systems to realise these challenging missions. The state-of-the- art SAR-based GMTI is often associated with multi-channel systems to improve the detection capabilities compared to the single-channel ones. Motivated by the fact that the SAR imaging is essentially solving an optimisation problem, we investigate the practicality to reformulate the GMTI process into the optimisation form. Furthermore, the moving target sparsities and underlying similarities between the conventional GMTI processing and sparse reconstruction algorithms drive us to consider the compressed sensing theory in SAR/GMTI applications. This thesis aims to establish an end-to-end SAR/GMTI processing framework regularised by target sparsities based on multi-channel SAR models. We have explained the mathematical model of the SAR system and its key properties in details. The common GMTI mechanism and basics of the compressed sensing theory are also introduced in this thesis. The practical implementation of the proposed framework is provided in this work. The developed model is capable of realising various SAR/GMTI tasks including SAR image formation, moving target detection, target state estimation and moving target imaging. We also consider two essential components, i.e. the data pre-processing and elevation map, in this work. The effectiveness of the proposed framework is demonstrated through both simulations and real data. Given that our focus in this thesis is on the development of a complete sparsity-aided SAR/GMTI framework, the contributions of this thesis can be summarised as follows. First, the effects of SAR channel balancing techniques and elevation information in SAR/GMTI applications are analysed in details. We have adapted these essential components to the developed framework for data pre-processing, system specification estimation and better SAR/GMTI accuracies. Although the purpose is on enhancing the proposed sparsity-based SAR/GMTI framework, the exploitation of the DEM in other SAR/GMTI algorithms may be of independent interest. Secondly, we have designed a novel sparsity-aided framework which integrates the SAR/GMTI missions, i.e. SAR imaging, moving target and background decomposition, and target state estimation, into optimisation problems. A practical implementation of the proposed framework with a two stage process and theoretically/experimentally proven algorithms are proposed in this work. The key novelty on utilising optimisations and target sparsities is explained in details. Finally, a practical algorithm for moving target imaging and state estimation is developed to accurately estimate the full target parameters and form target images with relocation and refocusing capabilities. Compared to the previous processing steps for practical applications, the designed algorithm consistently relies on the exploitation of target sparsities which forms the final processing stage of the whole pipeline. All the developed components contribute coherently to establish a complete sparsity driven SAR/GMTI processing framework

Ground moving target indication with synthetic aperture radars for maritime surveillance

The explosive growth of shipping traffic all over the World, with around three quarters of the total trade goods and crude oil transported by sea, has raised newly emerging concerns (economical, ecological, social and geopolitical). Geo-information (location and speed) of ocean-going vessels is crucial in the maritime framework, playing a key role in the related environmental monitoring, fisheries management and maritime/coastal security. In this scenario space-based synthetic aperture radar (SAR) remote sensing is a potential tool for globally monitoring the oceans and seas, providing two-dimensional high-resolution imaging capabilities in all-day and all-weather conditions. The combination of ground moving target indication (GMTI) modes with multichannel spaceborne SAR systems represents a powerful apparatus for surveillance of maritime activities. The level of readiness of such a technology for road traffic monitoring is still low, and for the marine scenario is even less mature. Some of the current space-based SAR missions include an experimental GMTI mode with reduced detection capabilities, especially for small and slow moving targets. In this framework, this doctoral dissertation focuses on the study and analysis of the GMTI limitations of current state-of-the-art SAR missions when operating over maritime scenarios and the proposal of novel and optimal multichannel SAR-GMTI architectures, providing subclutter visibility of small (reduced reflectivity) slow moving vessels. This doctoral activity carries out a transversal analysis embracing system-architecture proposal and optimization, processing strategies assessment, performance evaluation, sea/ocean clutter characterization and adequate calibration methodologies suggestion. Firstly, the scarce availability of multichannel SAR-GMTI raw data and the related restrictions to access it have raised the need to implement flexible simulation tools for SAR-GMTI performance evaluation and mission. These simulation tools allow the comparative study and evaluation of the SAR-GMTI mode operated with current SAR missions, showing the reduced ability of these missions to detect small and slow boats in subclutter visibility. Improved performance is achieved with the new multichannel architecture based on non-uniformly distributed receivers (with external deployable antennas), setting the ground for future SAR-GMTI mission development. Some experimental multichannel SAR-GMTI data sets over the sea and acquired with two instruments, airborne F-SAR and spaceborne TerraSAR-X (TSX) platforms, have been processed to evaluate their detection capabilities as well as the adequate processing strategies (including channel balancing). This doctoral activity presents also a preliminary characterization of the sea clutter returns imaged by the spaceborne TSX instrument in a three-level basis, i.e., radiometric, statistical and polarimetric descriptions using experimental polarimetric data. This study has shown that the system-dependent limitations, such as thermal noise and temporal decorrelation, play a key role in the appropriate interpretation of the data and so should be properly included in the physical backscattering models of the sea. Current and most of the upcoming SAR missions are based on active phase array antennas (APAA) technology for the operation of multiple modes of acquisitions. The related calibration is a complex procedure due to the high number of different beams to be operated. Alternative internal calibration methodologies have been proposed and analyzed in the frame of this doctoral thesis. These approaches improved the radiometric calibration performance compared to the conventional ones. The presented formulation of the system errors as well as the proposed alternative strategies set the path to extrapolate the analysis for multichannel SAR systems.L'increment continu del tràfic marítim arreu del món, amb gairebé tres quartes parts del total de mercaderies i cru transportats per mar, porta associats uns impactes canviants a nivell econòmic, ambiental, social i geopolític. La geo-informació (localització i velocitat) dels vaixells té un paper fonamental en el monitoratge ambiental, la gestió de la pesca i la seguretat marítima/costanera. Els radars d'obertura sintètica (SAR, sigles en anglès) embarcats en satèl·lits són una eina molt potent per al monitoratge global dels oceans i dels mars, gràcies a la seva capacitat de generar imatges d'alta resolució amb independència de les condicions meteorològiques i de la llum solar. La detecció de blancs mòbils terrestres (GMTI, sigles en anglès) combinada amb sistemes multicanal SAR és fonamental per a la vigilància de les activitats marítimes. El nivell de maduresa d'aquesta tecnologia per monitorar tràfic rodat és baix, però per al cas marítim encara ho és més. Algunes missions SAR orbitals inclouen el mode GMTI, però amb unes capacitats de detecció reduïdes, especialment per a blancs petits i lents. En aquest marc, la tesi doctoral es centra en l'estudi i anàlisi de les limitacions GMTI dels actuals sistemes SAR operant en entorns marítims, proposant noves configuracions SAR-GMTI multicanal optimitzades per a la detecció de vaixells petits (emmascarats pels retrons radar del mar) i que es mouen lentament. La present dissertació doctoral du a terme un estudi transversal que abasta des de la proposta i optimització de sistemes/configuracions, passant per l'avaluació de les tècniques de processat, fins a l'estudi del rendiment de la missió, caracterització del mar i la valoració de noves metodologies de calibratge. En primer terme, diverses eines de simulació flexibles s'han implementat per poder avaluar les capacitats GMTI de diferents missions tenint en compte la poca disponibilitat de dades multicanal SAR-GMTI. Aquests simuladors permeten l'estudi comparatiu de les capacitats GMTI de les missions SAR orbitals actuals, demostrant les seves reduïdes opcions per identificar vaixells emmascarats pels retorns del mar. En el marc de l'activitat de recerca s'han processat dades experimentals SAR-GMTI multicanal de sistemes aeris (F-SAR) i orbitals (TerraSAR-X), per tal d'avaluar les seves capacitats de detecció de blancs mòbils sobre entorns marítims, proposant les estratègies de processat i calibratge més adients. Com a part de l'activitat de recerca doctoral, s'ha portat a terme una caracterització preliminar dels retorns radar del mar adquirits amb el sensor orbital TerraSAR-X, amb tres nivells d'anàlisi (radiomètric, estadístic i polarimètric). Aquest estudi demostra que aspectes com el soroll tèrmic i la decorrelació temporal, dependents del propi sensor i de l'entorn dinàmic del mar, poden limitar la correcta interpretació de les dades, i per tant, s'han d'incloure en els models físics dels mecanismes de dispersió del mar. Les missions SAR tant actuals com futures es basen en l'explotació de la tecnologia de les agrupacions d'antenes de fase activa (APAA) per operar diferents modes d'adquisició. El procés de calibratge associat és molt complex atès el gran nombre de feixos que es poden utilitzar. En el marc de la tesi doctoral s'han proposat i avaluat metodologies alternatives de calibratge intern per aquests sistemes, amb un millor rendiment en comparació amb les tècniques convencionals. Aquestes estratègies de calibratge, juntament amb la corresponent formulació dels errors de sistema, estableixen les bases per a l'estudi i avaluació en sistemes multicanal SA

Convex Model-Based Synthetic Aperture Radar Processing

The use of radar often conjures up images of small blobs on a screen. But current synthetic aperture radar (SAR) systems are able to generate near-optical quality images with amazing benefits compared to optical sensors. These SAR sensors work in all weather conditions, day or night, and provide many advanced capabilities to detect and identify targets of interest. These amazing abilities have made SAR sensors a work-horse in remote sensing, and military applications. SAR sensors are ranging instruments that operate in a 3D environment, but unfortunately the results and interpretation of SAR images have traditionally been done in 2D. Three-dimensional SAR images could provide improved target detection and identification along with improved scene interpretability. As technology has increased, particularly regarding our ability to solve difficult optimization problems, the 3D SAR reconstruction problem has gathered more interest. This dissertation provides the SAR and mathematical background required to pose a SAR 3D reconstruction problem. The problem is posed in a way that allows prior knowledge about the target of interest to be integrated into the optimization problem when known. The developed model is demonstrated on simulated data initially in order to illustrate critical concepts in the development. Then once comprehension is achieved the processing is applied to actual SAR data. The 3D results are contrasted against the current gold- standard. The results are shown as 3D images demonstrating the improvement regarding scene interpretability that this approach provides

Seafloor depth estimation by means of interferometric synthetic aperture sonar

The topic of this thesis is relative depth estimation using interferometric sidelooking sonar. We give a thorough description of the geometry of interferometric sonar and of time delay estimation techniques. We present a novel solution for the depth estimate using sidelooking sonar, and review the cross-correlation function, the cross-uncertainty function and the phase-differencing technique. We find an elegant solution to co-registration and unwrapping by interpolating the sonar data in ground-range. Two depth estimation techniques are developed: Cross-correlation based sidescan bathymetry and synthetic aperture sonar (SAS) interferometry. We define flank length as a measure of the horizontal resolution in bathymetric maps and find that both sidescan bathymetry and SAS interferometry achieve theoretical resolutions. The vertical precision of our two methods are close to the performance predicted from the measured coherence. We study absolute phase-difference estimation using bandwidth and find a very simple split-bandwidth approach which outperforms a standard 2D phase unwrapper on complicated objects. We also examine advanced filtering of depth maps. Finally, we present pipeline surveying as an example application of interferometric SAS

Ground moving target tracking with space-time adaptive radar

Ground moving target tracking by airborne radar provides situational awareness of vehicle movements in the supervised region. Vehicles are detected by applying space time adaptive processing to the received multi channel radar data. The detections are then fed to a tracking algorithm that processes them to tracks. In literature, radar signal processing and ground target tracking are treated as two separate topics and results are not validated by experimental data. The first objective of this thesis is to provide a closer link between these fields. The second objective is to show that tracking performance can be improved by providing additional data from the radar signal processing to the tracking step. The third objective is to validate the algorithm and the performance improvement using experimental data. As a result this thesis presents a unified treatment of ground moving target tracking from radar raw data to established tracks. A complete reference algorithm for ground moving target tracking based on the Gaussian mixture probability hypothesis density filter is presented. In particular, Jacobians of the observation process are derived. They are presented in such a form that immediate implementation in a programming language is possible. In the course of this thesis a measurement campaign with the experimental radar PAMIR of Fraunhofer FHR was conducted. The experiment included two GPS equipped reference vehicles and a multitude of targets of opportunity. Tracking results obtained with this experimental data and the reference tracking algorithm of this thesis are shown. The thesis also enhances the reference target tracking algorithm by a parameter that characterizes the variance of the direction of arrival measurement of the target signal. This parameter is determined adaptively depending on the estimated signal strength and the clutter background. The major contribution with regard to this enhancement is a thorough experimental validation: Firstly, a comparison between GPS based measurements and radar based measurements of the direction of arrival shows that this variance captures the distribution of measurement errors excellently. Secondly, tracking results are compared to the GPS tracks of the ground truth vehicles. It is found that the enhanced algorithm yields superior track quality with respect to both track accuracy and track continuity.Bodenzielverfolgung mit luftgestütztem Radar liefert das Lagebild von Fahrzeug­bewegungen innerhalb des beobachteten Gebiets. Fahrzeuge werden durch die Anwendung von Raum-Zeit adaptiver Signalverarbeitung (STAP) entdeckt. Die Entdeckungen werden dann von einem Zielverfolgungsalgorithmus zu Zielspuren verarbeitet. In der Literatur werden Radarsignalverarbeitung und Zielverfolgung als zwei getrennte Forschungsfelder behandelt und die Bodenzielverfolgung wird nicht anhand von Realdaten validiert. Das erste Ziel dieser Arbeit ist, eine engere Verbindung zwischen beiden Feldern herzustellen. Das zweite Ziel ist zu zeigen, dass die Qualität der Zielverfolgung durch das Verwenden zusätzlicher, durch die Radarsignalverarbeitung gewonnene Information verbessert werden kann. Das dritte Ziel ist, die Funktionalität der Zielverfolgung und die Verbesserung der Leistung durch experimentelle Realdaten zu belegen. Somit stellt diese Arbeit eine Gesamtbehandlung der Bodenzielverfolgung von den Radar-Rohdaten bis zu Zielspuren dar. Es wird ein vollständiger, auf dem Gaussian Mixture Probability Hypothesis Density Filter basierender Referenzalgorithmus für die Bodenzielverfolgung entwickelt. Insbesondere werden Jacobimatrizen der Beobachtungsfunktion hergeleitet. Sie werden in der Arbeit so dargestellt, dass sie direkt in einer Programmiersprache implementiert werden können. Im Zuge dieser Arbeit wurde ein Zielverfolgungs-Experiment mit dem Experimentalsystem PAMIR des Fraunhofer FHR durchgeführt. In dem Experiment wurden neben einer Vielzahl von Gelegenheitszielen zwei mit GPS-Geräten ausgerüstete Fahrzeuge von dem Radar beobachtet. Auf Basis dieses Experiments und des Referenzalgorithmus werden Zielverfolgungsergebnisse vorgestellt. Darüber hinaus erweitert diese Arbeit den Referenzalgorithmus um einen Parameter, der die Varianz der Richtungsschätzung des Zielsignals charakterisiert. Dieser Parameter wird adaptiv anhand der geschätzten Signalstärke und der Stärke störender Bodenrückstreuungen festgelegt. Der wesentliche Beitrag dieser Arbeit in Bezug auf diese Erweiterung ist eine gründliche experimentelle Validierung. Erstens zeigt der Vergleich von GPS- und Radar-basierten Richtungsschätzungen, dass dieser Parameter die Verteilung des Messfehlers exzellent beschreibt. Zweitens werden Zielverfolgungsergebnisse mit den GPS-Spuren verglichen. Es zeigt sich, dass der erweiterte Algorithmus sowohl in Bezug auf die Spurgenauigkeit als auch in Bezug auf die Spurkontinuität die Zielverfolgung verbessert

Synthetic aperture radar/LANDSAT MSS image registration

Algorithms and procedures necessary to merge aircraft synthetic aperture radar (SAR) and LANDSAT multispectral scanner (MSS) imagery were determined. The design of a SAR/LANDSAT data merging system was developed. Aircraft SAR images were registered to the corresponding LANDSAT MSS scenes and were the subject of experimental investigations. Results indicate that the registration of SAR imagery with LANDSAT MSS imagery is feasible from a technical viewpoint, and useful from an information-content viewpoint

Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Three Dimensional Bistatic Tomography Using HDTV

The thesis begins with a review of the principles of diffraction and reflection tomography; starting with the analytic solution to the inhomogeneous Helmholtz equation, after linearization by the Born approximation (the weak scatterer solution), and arriving at the Filtered Back Projection (Propagation) method of reconstruction. This is followed by a heuristic derivation more directly couched in the radar imaging context, without the rigor of the general inverse problem solution and more closely resembling an imaging turntable or inverse synthetic aperture radar. The heuristic derivation leads into the concept of the line integral and projections (the Radon Transform), followed by more general geometries where the plane wave approximation is invalid. We proceed next to study of the dependency of reconstruction on the space-frequency trajectory, combining the spatial aperture and waveform. Two and three dimensional apertures, monostatic and bistatic, fully and sparsely sampled and including partial apertures, with controlled waveforms (CW and pulsed, with and without modulation) define the filling of k-space and concomitant reconstruction performance. Theoretical developments in the first half of the thesis are applied to the specific example of bistatic tomographic imaging using High Definition Television (HDTV); the United States version of DVB-T. Modeling of the HDTV waveform using pseudonoise modulation to represent the hybrid 8VSB HDTV scheme and the move-stop-move approximation established the imaging potential, employing an idealized, isotropic 18 scatterer. As the move-stop-move approximation places a limitation on integration time (in cross correlation/pulse compression) due to transmitter/receiver motion, an exact solution for compensation of Doppler distortion is derived. The concept is tested with the assembly and flight test of a bistatic radar system employing software-defined radios (SDR). A three dimensional, bistatic collection aperture, exploiting an elevated commercial HDTV transmitter, is focused to demonstrate the principle. This work, to the best of our knowledge, represents a first in the formation of three dimensional images using bistatically-exploited television transmitters