Synthetic Aperture Radar 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.

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.

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.

Compression of synthetic aperture radar video phase history data using trellis-coded quantization techniques

Synthetic aperture radar (SAR) is a remote-sensing technology that uses the motion of the radar transmitter to synthesize an antenna aperture much larger than the actual antenna aperture to yield high-spatial resolution radar images. In this paper, trellis-coded quantization (TCQ) techniques are shown to provide a high-performance, low bit-error sensitivity solution to the problem of downlink data rate reduction for SAR systems. Trellis-coded vector quantization (TCVQ) and universal TCQ coding systems are discussed, implemented, and compared with other data compression schemes [block adaptive quantization (BAQ) and VQ] that can be used to compress SAR phase history data.

Rate allocation for spotlight SAR phase history data compression

Complex phase history data in synthetic aperture radar (SAR) systems require extensive processing before useful images can be obtained. In spotlight mode SAR systems, useful images can be obtained by applying aperture weighting and inverse Fourier transform operations to SAR phase history data. In this paper, we are concerned with the compression of the complex phase history data obtained by a spotlight SAR system. We exploit knowledge of the aperture weighting function along with Fourier transform processing to attach a "gain" factor to each complex phase history data sample. This gain factor is then used to efficiently allocate bits to the phase history data during quantization. Performance evaluations are presented for this compression system relative to other existing SAR phase history data compression systems.

Nonlinear techniques in optical synthetic aperture radar image generation and target recognition

One of the most successful optical signal-processing applications to date has been the use of optical processors to convert synthetic aperture radar (SAR) data into images of the radar reflectivity of the ground. We have demonstrated real-time input to a high-space-bandwidth optical SAR imagegeneration system by using a dynamic organic holographic recording medium and SAR phase-history data. Real-time speckle reduction in optically processed SAR imagery has been accomplished by the use of multilook averaging to achieve nonlinear modulus-squared accumulation of subaperture images. We designed and assembled an all-optical system that accomplished real-time target recognition in SAR imagery. This system employed a simple square-law nonlinearity in the form of an optically addressed spatial light modulator at the SAR image plane to remove the effects of speckle phase profiles returned from complex SAR targets. The detection stage enabled the creation of an optical SAR automatic target recognition system as a nonlinear cascade of an optical SAR image generator and an optical correlator.