An estimation-theoretic technique for motion-compensated synthetic-aperture array imaging

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Vita.Includes bibliographical references (p. 351-354).Synthetic-Aperture Radar (SAR) is an imaging technique that achieves high azimuth resolution by using coherent processing to exploit the relative motion between an airborne or spaceborne radar antenna and the imaged target field (effectively synthesizing the effect of a larger aperture array). From an estimation-theoretic perspective, this thesis addresses the following limitations of conventional imaging techniques for the spotlight-mode version of SAR: sidelobe imaging artifacts and loss of resolution for stationary SAR scenes containing high-amplitude scatterers, and blurring and object-displacement artifacts in the presence of moving targets. First, this thesis presents a generalized estimation-theoretic SAR imaging framework which exploits the idea of L1-norm regularization. Some results are included which demonstrate the utility of this approach for reducing sidelobes and improving resolution for stationary SAR images. A parameterized L-norm-based moving-target imaging technique is also presented. For the case of a single moving target, this technique is able to compensate for the blurring due to temporally-constant velocity rigid-body motion (even if the target scatterers are closely-spaced). However, the motion-induced object-displacement compensation performance of this technique is significantly affected by velocity estimation errors. This thesis also presents an estimation-theoretic moving-target SAR imaging framework which uses a multi-dimensional matched-filter for computing a set of scatterer-velocity estimates which are used as initial conditions for an L1-norm-based estimation algorithm which assumes that the target scatterers have temporally-constant spatially-independent velocities. Therefore, this framework is able to image a moving target and nearby high-amplitude stationary clutter simultaneously. This framework also shows potential for imaging targets with non-rigid body motion. However, the motion-induced object-displacement compensation performance of this approach is significantly affected by cross-scatterer interference effects.by Cedric Leonard Logan.Sc.D

Blind Deconvolution of Anisoplanatic Images Collected by a Partially Coherent Imaging System

Coherent imaging systems offer unique benefits to system operators in terms of resolving power, range gating, selective illumination and utility for applications where passively illuminated targets have limited emissivity or reflectivity. This research proposes a novel blind deconvolution algorithm that is based on a maximum a posteriori Bayesian estimator constructed upon a physically based statistical model for the intensity of the partially coherent light at the imaging detector. The estimator is initially constructed using a shift-invariant system model, and is later extended to the case of a shift-variant optical system by the addition of a transfer function term that quantifies optical blur for wide fields-of-view and atmospheric conditions. The estimators are evaluated using both synthetically generated imagery, as well as experimentally collected image data from an outdoor optical range. The research is extended to consider the effects of weighted frame averaging for the individual short-exposure frames collected by the imaging system. It was found that binary weighting of ensemble frames significantly increases spatial resolution

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed

Radar Imaging Based on IEEE 802.11ad Waveform

The extension to millimeter-wave (mmWave) spectrum of communication frequency band makes it easy to implement a joint radar and communication system using single hardware. In this paper, we propose radar imaging based on the IEEE 802.11ad waveform for a vehicular setting. The necessary parameters to be estimated for inverse synthetic aperture radar (ISAR) imaging are sampled version of round-trip delay, Doppler shift, and vehicular velocity. The delay is estimated using the correlation property of Golay complementary sequences embedded on the IEEE 802.11ad preamble. The Doppler shift is first obtained from least square estimation using radar return signals and refined by correcting the phase uncertainty of Doppler shift by phase rotation. The vehicular velocity is determined from the estimated Doppler shifts and an equation of motion. Finally, an ISAR image is formed with the acquired parameters. Simulation results show that it is possible to obtain recognizable ISAR image from a point scatterer model of a realistic vehicular setting.Comment: 6 pages, 6 figures, and accepted for 2020 IEEE Global Communications Conference (GLOBECOM

PRECONDITIONING AND THE APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS TO CLASSIFY MOVING TARGETS IN SAR IMAGERY

Synthetic Aperture Radar (SAR) is a principle that uses transmitted pulses that store and combine scene echoes to build an image that represents the scene reflectivity. SAR systems can be found on a wide variety of platforms to include satellites, aircraft, and more recently, unmanned platforms like the Global Hawk unmanned aerial vehicle. The next step is to process, analyze and classify the SAR data. The use of a convolutional neural network (CNN) to analyze SAR imagery is a viable method to achieve Automatic Target Recognition (ATR) in military applications. The CNN is an artificial neural network that uses convolutional layers to detect certain features in an image. These features correspond to a target of interest and train the CNN to recognize and classify future images. Moving targets present a major challenge to current SAR ATR methods due to the “smearing” effect in the image. Past research has shown that the combination of autofocus techniques and proper training with moving targets improves the accuracy of the CNN at target recognition. The current research includes improvement of the CNN algorithm and preconditioning techniques, as well as a deeper analysis of moving targets with complex motion such as changes to roll, pitch or yaw. The CNN algorithm was developed and verified using computer simulation.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Interferometric synthetic aperture sonar system supported by satellite

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

A Generalized Phase Gradient Autofocus Algorithm

The phase gradient autofocus (PGA) algorithm has seen widespread use and success within the synthetic aperture radar (SAR) imaging community. However, its use and success has largely been limited to collection geometries where either the polar format algorithm (PFA) or range migration algorithm is suitable for SAR image formation. In this work, a generalized phase gradient autofocus (GPGA) algorithm is developed which is applicable with both the PFA and backprojection algorithm (BPA), thereby directly supporting a wide range of collection geometries and SAR imaging modalities. The GPGA algorithm preserves the four crucial signal processing steps comprising the PGA algorithm, while alleviating the constraint of using a single scatterer per range cut for phase error estimation which exists with the PGA algorithm. Moreover, the GPGA algorithm, whether using the PFA or BPA, yields an approximate maxi- mum marginal likelihood estimate (MMLE) of phase errors having marginalized over unknown complex-valued reflectivities of selected scatterers. Also, in this work a new approximate MMLE, termed the max-semidefinite relaxation (Max-SDR) phase estimator, is proposed for use with the GPGA algorithm. The Max-SDR phase estimator provides a phase error estimate with a worst-case approximation bound compared to the solution set of MMLEs (i.e., solution set to the non-deterministic polynomial- time hard (NP-hard) GPGA phase estimation problem). Moreover, in this work a specialized interior-point method is presented for more efficiently performing Max- SDR phase estimation by exploiting low-rank structure typically associated with the GPGA phase estimation problem. Lastly, simulation and experimental results produced by applying the GPGA algorithm with the PFA and BPA are presented

Imaging of moving targets with multi-static SAR using an overcomplete dictionary

This paper presents a method for imaging of moving targets using multi-static SAR by treating the problem as one of spatial reflectivity signal inversion over an overcomplete dictionary of target velocities. Since SAR sensor returns can be related to the spatial frequency domain projections of the scattering field, we exploit insights from compressed sensing theory to show that moving targets can be effectively imaged with transmitters and receivers randomly dispersed in a multi-static geometry within a narrow forward cone around the scene of interest. Existing approaches to dealing with moving targets in SAR solve a coupled non-linear problem of target scattering and motion estimation typically through matched filtering. In contrast, by using an overcomplete dictionary approach we effectively linearize the forward model and solve the moving target problem as a larger, unified regularized inversion problem subject to sparsity constraints.Comment: This work has been submitted to IEEE Journal on Selected Topics in Signal Processing (Special Issue on MIMO Radar and Its Applications) for possible publicatio

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour