Phase History Data Research Articles

Sub-aperture SAR imaging with uncertainty quantification

In the problem of spotlight mode airborne synthetic aperture radar (SAR) image formation, it is well-known that data collected over a wide azimuthal angle violate the isotropic scattering property typically assumed. Many techniques have been proposed to account for this issue, including both full-aperture and sub-aperture methods based on filtering, regularized least squares, and Bayesian methods. A full-aperture method that uses a hierarchical Bayesian prior to incorporate appropriate speckle modeling and reduction was recently introduced to produce samples of the posterior density rather than a single image estimate. This uncertainty quantification information is more robust as it can generate a variety of statistics for the scene. As proposed, the method was not well-suited for large problems, however, as the sampling was inefficient. Moreover, the method was not explicitly designed to mitigate the effects of the faulty isotropic scattering assumption. In this work we therefore propose a new sub-aperture SAR imaging method that uses a sparse Bayesian learning-type algorithm to more efficiently produce approximate posterior densities for each sub-aperture window. These estimates may be useful in and of themselves, or when of interest, the statistics from these distributions can be combined to form a composite image. Furthermore, unlike the often-employed -regularized least squares methods, no user-defined parameters are required. Application-specific adjustments are made to reduce the typically burdensome runtime and storage requirements so that appropriately large images can be generated. Finally, this paper focuses on incorporating these techniques into SAR image formation process, that is, for the problem starting with SAR phase history data, so that no additional processing errors are incurred. The advantage over existing SAR image formation methods are clearly presented with numerical experiments using real-world data.

Sampling-based Spotlight SAR Image Reconstruction from Phase History Data for Speckle Reduction and Uncertainty Quantification

Spotlight mode airborne synthetic aperture radar (SAR) is a coherent imaging modality that is an important tool in remote sensing. Existing methods for spotlight SAR image reconstruction from phase history data typically produce a single image estimate which approximates the reflectivity of an unknown ground scene, and therefore provide no quantification of the certainty with which the estimate can be trusted. In addition, speckle affects all coherent imaging modalities causing a degradation of image quality. Many point estimate image reconstruction methods incorrectly treat speckle as additive noise resulting in an unnatural smoothing of the speckle that also reduces image contrast. The purpose of this paper is to address the issues of speckle and uncertainty quantification by introducing a sampling-based approach to SAR image reconstruction directly from phase history data. In particular, a statistical model for speckle as well as a corresponding sparsity technique to reduce it are directly incorporated into the model. Rather than a single point estimate, samples of the resulting joint posterior density are efficiently obtained using a Gibbs sampler, which are in turn used to derive estimates and other statistics which aid in uncertainty quantification. The latter information is particularly important in SAR, where ground truth images even for synthetically created examples are typically unknown. While similar methods have been deployed to process formed images, this paper focuses on the integration of these techniques into image reconstruction from phase history data. An example result using real-world data shows that, when compared with existing methods, the sampling-based approach introduced provides parameter-free estimates with improved contrast and significantly reduced speckle, as well as uncertainty quantification information.

Architecture Exploration of a Backprojection Algorithm for Real-Time Video SAR.

This paper explores novel architectures for fast backprojection based video synthetic aperture radar (BP-VISAR) with multiple GPUs. The video SAR frame rate is analyzed for non-overlapped and overlapped aperture modes. For the parallelization of the backprojection process, a processing data unit is defined as the phase history data or range profile data from partial synthetic-apertures divided from the full resolution target data. Considering whether full-aperture processing is performed and range compression or backprojection are parallelized on a GPU basis, we propose six distinct architectures, each having a single-stream pipeline with a single GPU. The performance of these architectures is evaluated in both non-overlapped and overlapped modes. The efficiency of the BP-VISAR architecture with sub-aperture processing in the overlapped mode is accelerated further by filling the processing gap from the idling GPU resources with multi-stream based backprojection on multiple GPUs. The frame rate of the proposed BP-VISAR architecture with sub-aperture processing is scalable with the number of GPU devices for large pixel resolution. It can generate 4096 × 4096 video SAR frames of 0.5 m cross-range resolution in 23.0 Hz on a single GPU and 73.5 Hz on quad GPUs.

Plug-and-Play Synthetic Aperture Radar Image Formation Using Deep Priors

The reconstruction of synthetic aperture radar (SAR) images from phase history data is an ill-posed inverse problem which, in several lines of recent work, is solved by minimizing a cost function. Existing reconstruction methods use regularization to tackle the ill-posed nature of the imaging task. However, in general, these regularizers are either too simple to capture complex spatial patterns and can only promote fixed, predefined features, or lead to non-quadratic cost functions that are nontrivial to minimize. Recently emerging plug-and-play (PnP) priors technique is a flexible framework that allows forward models of imaging systems to be integrated with state-of-the-art regularizers. Inspired by this, we propose a novel PnP SAR image reconstruction framework for spotlight-mode SAR. SAR involves complex-valued reflectivities with spatial structure on the reflectivity magnitudes that can be learned and imposed as priors. This distinguishing aspect is formulated into our proposed framework. We demonstrate the use and effectiveness of a convolutional neural network (CNN) based prior model for the reconstruction of synthetic and real SAR scenes and compare the results with FFT-based, non-quadratic regularization-based, and dictionary learning-based reconstruction methods as well as a PnP framework with BM3D regularizer. Our results suggest that these deep priors enable the learning and incorporation of complicated spatial patterns more effectively than existing methods, and produce significantly improved images especially from limited observations.

Algorithm to overcome atmospheric phase errors in SAL data.

Synthetic aperture ladar is an emerging sensor technology providing high-resolution imagery of targets from long standoff ranges. Atmospheric turbulence corrupts the collected phase history data with spatially variant phase perturbations, impacting resolution and contrast of reconstructed imagery. We explore the efficacy of model-based reconstruction algorithms with model error corrections to mitigate the deleterious effects of atmospheric turbulence and restore image quality. We present results from model error correction techniques utilizing spatially invariant, spatially variant, and a model-based atmospheric phase error correction. We quantify the performance of all algorithms using an atmospheric ray-trace simulation.

Squint-looking differential synthetic aperture ladar: signal processing and experimental demonstration.

Squint-looking differential synthetic aperture ladar (DSAL) is reported with detailed signal processing mathematics and high-resolution experimental demonstrations. Based on the DSAL principle and standard squint-looking synthetic aperture radar theory, the data processing procedures on squint-looking DSAL image formation are obtained. The experimental DSAL setup, operating in "step-stop" strip map mode, adopts a frequency chirped laser with a wavelength of 1550 nm as the illuminating source and a specially designed random phase generator to introduce large common mode random phase error (RPE) into the phase history data of both receiving sub-apertures. High-resolution DSAL images of a cooperative target at a distance of 1.85 m and squint-looking angle of $-{10}^\circ$-10∘ or $+{10}^\circ$+10∘ are demonstrated. The DSAL images, with or without large RPE, are all well focused by straightforwardly following the given data processing steps. The result illustrates that the DSAL technique is robust in removing common mode phase errors in squint-looking configuration.

An Autofocus Cartesian Factorized Backprojection Algorithm for Spotlight Synthetic Aperture Radar Imaging

A backprojection (BP) algorithm is recognized as an ideal method for high-resolution synthetic aperture radar (SAR) imaging. Several fast BP algorithms have been developed to enhance the efficiency of the BP integral. The Cartesian factorized BP (CFBP) algorithm is proposed recently to avoid massive interpolations and improve the performance. However, integrating autofocus techniques with the CFBP has not been discussed. In this letter, an autofocus CFBP algorithm is proposed to compatibly combine the autofocus processing within the CFBP. After modifying the spectrum compression step in the CFBP, the approximate Fourier transformation (FT) relationship between the modified compensated subaperture images and the corresponding range-compressed phase history data in the Cartesian coordinate is revealed. The phase error is obtained by the multiple aperture map drift method, and the singular value decomposition total least square method is combined to improve the estimate robustness. Employing the range blocking method, the range variance of the phase error is compensated. The proposed algorithm inherits the advantages of the CFBP. Experiments performed by the X-band airborne SAR system with a maximum bandwidth of 1.2 GHz validate the proposed approaches.

Performance of differential synthetic aperture ladar versus phase-gradient autofocus at large phase errors and low return levels

The performance of differential synthetic aperture ladar (DSAL) processing versus phase-gradient autofocus (PGA) technique in SAL image formation is demonstrated experimentally using phase history data (PHD) with large random phase errors and various target returns generated from a laboratory DSAL setup operating at 1550-nm wavelength. Results show that at strong return levels, both DSAL and PGA are very robust in focusing high-resolution images from the PHD; whereas, as target returns become weak and PHD is dominated by noise, the iterative PGA performs less effectively than the DSAL does. It is concluded that, on the whole, the DSAL technique is possibly more robust than PGA in both effectiveness and efficiencies.

Bistatic-ISAR Distortion Correction and Range and Cross-Range Scaling

The bistatic configuration used for inverse synthetic aperture radar (ISAR) imaging has attracted much attention because of its potential to overcome the certain limitations of monostatic ISAR imaging. However, range and cross-range scaling and bistatic distortion correction are essential for the efficient use of a bistatic ISAR (Bi-ISAR) image in their applications. In this paper, we introduce a novel range and cross-range scaling technique and bistatic distortion correction method for the restoration of a sheared (Bi-ISAR) image. The proposed method is composed of five stages: 1) extraction of the prominent scatterer using the range-bin selection method; 2) determination of the coefficients of the phase-history data in the selected range bin; 3) estimation of rotation center of the target using the optimization procedures via the particle swarm optimization algorithm; 4) range and cross-range scaling of (Bi-ISAR) image; and 5) restoration of the bistatic distortion that caused the sheared (Bi-ISAR) image. Finally, the proposed method can generate a well-scaled and restored (Bi-ISAR) image. An outstanding advantage of this method is that it requires no prior information about the bistatic configuration. Several examples are presented to validate the effectiveness of the proposed method.

Imaging method for airborne SAL data

Synthetic aperture ladar (SAL) is analogous to the synthetic aperture radar (SAR) but works at wavelength 104-5 times shorter. SAL has the potential for much finer resolution than SAR. Due to the shorter wavelength used for SAL, the platform vibration would induce high-frequency and large-amplitude phase errors during the data collection. Direct using of the conventional imaging algorithms to SAL data suffers from performance degradation. To address this problem, an effective imaging method based on subaperture phase gradient autofocus (PGA) and normalised cross correlation (NCC) is proposed for airborne SAL data. First, the length of subaperture is determined according to the criterion that the coherent processing interval (CPI) is shorter than half of the vibration period. Then the phase history data (PHD) is formatted into a few subapertures and the PGA is employed to estimate the phase errors in each subaperture. After the compensation of the phase errors, each subaperture image is focused with the conventional imaging algorithm. Finally, a recombination of the subapertures is performed based on the NCC. The proposed method is verified by processing the real SAL data collected by strip-mode with the demonstrator system built at Institute of Electronics, Chinese Academy of Science.

Experimental demonstration of tri-aperture Differential Synthetic Aperture Ladar

A tri-aperture Differential Synthetic Aperture Ladar (DSAL) is demonstrated in laboratory, which is configured by using one common aperture to transmit the illuminating laser and another two along-track receiving apertures to collect back-scattered laser signal for optical heterodyne detection. The image formation theory on this tri-aperture DSAL shows that there are two possible methods to reconstruct the azimuth Phase History Data (PHD) for aperture synthesis by following standard DSAL principle, either method resulting in a different matched filter as well as an azimuth image resolution. The experimental setup of the tri-aperture DSAL adopts a frequency chirped laser of about 40mW in 1550nm wavelength range as the illuminating source and an optical isolator composed of a polarizing beam-splitter and a quarter wave plate to virtually line the three apertures in the along-track direction. Various DSAL images up to target distance of 12.9m are demonstrated using both PHD reconstructing methods.

Influence of Estimate Error of Radar Kinematic Parameter on Deceptive Jamming Against SAR

The influence of intelligence gathering inaccuracy, especially the estimate error of radar kinematic parameter, on the performance of deceptive jamming against synthetic aperture radar (SAR) is studied in this paper, filling a knowledge vacancy in the field of SAR electronic countermeasures. For the first time, an equivalence relationship between non-ideal jamming and ground moving target is derived and verified in raw phase history data domain and radar image domain. A novel and elegant way to quantify the defocus and displacement of false target in radar image is proposed based on the equivalence relationship. Work presented in this paper can guide the design of jammer's intelligence gathering subsystem and optimize jamming configuration, from the view of jammer. The results also avail electronic counter-countermeasures, from the view of radar.

Application of Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads


 
 
 Synthetic aperture radars are radar platforms that generate detailed images through radio frequency transmission and receiving. These systems can be high peak power, complex systems that can suffer from internal component or subsystem degradation. In addition, the operational environment can also affect the final image of the radar due to scene-based radio frequency interference (RFI). Because of these effects, it is ideal to be able to identify, classify, and quantify the degradation of these systems in order to optimize their performance and life. The work presented in this paper is an extension of QorTek’s previous work using Symbolic Analysis to detect degradation using the radar’s phase history data. In conjunction with the KEYW, Corp., QorTek has acquired field data to train and test its algorithm. To test the trained algorithm, a prototype hardware/software system integrating the SA approach was designed, built and flown on a test flight piggybacking on a radar system provided by KEYW. The initial results were very positive and also identified areas of improvement. The training and test results as well as the flight-test plan and results are presented in this paper. The paper concludes with specific improvements to be made to the algorithm for the next round of radar integration and flight-testing.
 
 

Theory of Two-Dimensional Signature Morphology for Arbitrarily Moving Surface Targets in Squinted Spotlight Synthetic Aperture Radar

This paper develops analytic equations for predicting the smear signature morphology due to surface targets having arbitrary motion within squinted spotlight synthetic aperture radar (SAR) imagery. A power-series expansion of the subaperture phase history data is applied to compute a generic expression for the down-range and cross-range components of the predicted central 2-D contour of mover signatures, including the cross-range offset. In addition, the current analysis generates an analytic approximation for the 2-D impulse response (IPR) of surface moving target signatures within subaperture images. This investigation reveals that the summation of a large number of nonoverlapping IPRs yields an excellent reproduction of the target signature, including smear width and self-interference effects, as compared with standard image formation using simulated radar data. Thus, the analytics derived herein can provide an effective methodology for understanding the shape, extent, location, width, and interference effects of smears due to arbitrarily moving surface targets for squinted spotlight SAR.

Health Management and Diagnostics for Synthetic Aperture Radar (SAR) Payloads

A statistical method based on symbolic analysis is presented for health management of Synthetic Aperture Radar systems. The approach, based on symbolic theory, develops statistical models of the underlying system dynamics using an underlying Markov assumption and tracks the change in model over time to determine system health. The methodology was designed for minimal impact to legacy systems and required minimal computational effort in order to operate at radar data rates. The approach was applied to radar phase history data corrupted with simulated degradation. Two degradation mechanisms were studied: interference and array degradation. In addition, the results of combined degradation were also studied in this work.

Interrupted SAR persistent surveillance via group sparse reconstruction of multipass data

In this paper we present a new method for synthetic aperture radar (SAR) image formation from interrupted, multipass SAR phase history data, with application to persistent surveillance SAR imaging. We propose a new compressed sensing-motivated approach to reconstruction that jointly processes multipass interrupted data using a sparse recovery technique with a group support constraint and results in improved imagery. We compare our approach, a group sparsity (GS) algorithm, to methods that independently process each data pass, namely the basis pursuit denoising and iterative adaptive approach methods. We find that the joint processing of GS results in coherent change detection gains over the other approaches regardless of interrupt pattern. To illustrate the capabilities of GS, we evaluate coherent change detection performance using images from the Gotcha SAR.

Integrating Autofocus Techniques With Fast Factorized Back-Projection for High-Resolution Spotlight SAR Imaging

Back-projection (BP) is considered as an ideal methodology for the high-resolution synthetic aperture radar (SAR) imaging. However, applying conventional autofocus techniques to BP imagery requires a special consideration and is usually difficult to implement. In this letter, we present a scheme to compatibly blending a novel multiple aperture map drift (MAMD) algorithm with fast factorized back-projection (FFBP). Through an elaborate BP coordinate, we construct the Fourier transform relationship between FFBP sub-aperture (SA) images and the corresponding range-compressed phase history data. The phase error function is achieved by the MAMD within FFBP recursions, and well-focused imagery is obtained by phase correction on the range-compressed phase history data. The proposed scheme inherits the advantages of high precision and efficiency of the FFBP, and is suitable for high-resolution spotlight SAR imaging with raw data. Real data experiments guarantee the effectiveness of our proposed scheme.

Autofocus algorithm for synthetic aperture radar imaging with large curvilinear apertures

An approach to autofocusing for large curved synthetic aperture radar (SAR) apertures is presented. Its essential feature is that phase corrections are being extracted not directly from SAR images, but rather from reconstructed SAR phase-history data representing windowed patches of the scene, of sizes sufficiently small to allow the linearization of the forward- and back-projection formulae. The algorithm processes data associated with each patch independently and in two steps. The first step employs a phase-gradient-type method in which phase correction compensating (possibly rapid) trajectory perturbations are estimated from the reconstructed phase history for the dominant scattering point on the patch. The second step uses phase-gradient-corrected data and extracts the absolute phase value, removing in this way phase ambiguities and reducing possible imperfections of the first stage, and providing the distances between the sensor and the scattering point with accuracy comparable to the wavelength. The features of the proposed autofocusing method are illustrated in its applications to intentionally corrupted small-scene 2006 Gotcha data. The examples include the extraction of absolute phases (ranges) for selected prominent point targets. They are then used to focus the scene and determine relative target–target distances.

Non-Parametric High-Resolution SAR Imaging

The development of high-resolution two-dimensional spectral estimation techniques is of notable interest in synthetic aperture radar (SAR) imaging. Typically, data-independent techniques are exploited to form the SAR images, although such approaches will suffer from limited resolution and high sidelobe levels. Recent work on data-adaptive approaches have shown that both the iterative adaptive approach (IAA) and the sparse learning via iterative minimization (SLIM) algorithm offer excellent performance with high-resolution and low side lobe levels for both complete and incomplete data sets. Regrettably, both algorithms are computationally intensive if applied directly to the phase history data to form the SAR images. To help alleviate this, efficient implementations have also been proposed. In this paper, we further this work, proposing yet further improved implementation strategies, including approaches using the segmented IAA approach and the approximative quasi-Newton technique. Furthermore, we introduce a combined IAA-MAP algorithm as well as a hybrid IAA- and SLIM-based estimation scheme for SAR imaging. The effectiveness of the SAR imaging algorithms and the computational complexities of their fast implementations are demonstrated using the simulated Slicy data set and the experimentally measured GOTCHA data set.

Bistatic Polar Format Algorithm Based on NUFFT Method

Matched filtering of phase history data is the optimum reconstruction algorithm to bistatic synthetic aperture radar (SAR) image formation. However, it comes at great computational expense scaled with complexity O (M 2 N 2), where M N is the number of pixels in the image. Based on the plane wave assumption, bistatic polar format algorithm (PFA) approximates the matched filtering kernel appropriately, so that the computational complexity in image formation is considerably reduced. In this paper, we propose a novel PFA for bistatic SAR processing. This imaging algorithm is to use non-uniform fast Fourier transform (NUFFT) to compute the matched filtering reconstruction image according the plane wave signal model of bistatic SAR, which scales with complexity O (M Nlog2 M N). Numerical simulations are performed to validate the proposed algorithm. The results prove that, compared with the direct computing of matched filtering, the presented algorithm can provide almost identical imaging performance in a relatively very short computation time.