GTD-based scattering models for bistatic SAR

ABSTRACT This paper discusses development of physics-based models for bistatic scattering. We generalize parametric equations for monostatic scattering mechanisms in a plane to achieve analogous bistatic approximations. Combination of these mechanisms, as separable azimuth and elevation components, allows 3-D modelling of six scattering primitives: sphere, tophat, trihedral, dihedral, cylinder, and flat plate. The responses of these scattering center models are shown to compare favorably with results obtained from validated high-frequency simulations

Hardware Development and Error Characterisation for the AFIT RAIL SAR System

This research is focussed on updating the Air Force Institute of Technology (AFIT) Radar Instrumentation Lab (RAIL) Synthetic Aperture Radar (SAR) experimental system. Firstly, this research assesses current hardware limitations and updates the system configuration and methodology to enable collections from a receiver in motion. Secondly, orthogonal frequency-division multiplexing (OFDM) signals are used to form (SAR) images in multiple experimental and simulation configurations. This research analyses, characterises and attempts compensation of relevant SAR image error sources, such as Doppler shift or motion measurement errors (MMEs). Error characterisation is conducted using theoretical, simulated and experimental methods. Final experimental results are presented to verify performance of the updated SAR collection system and show improvements to the final product through an updated methodology and various signal processing techniques

Self-correcting multi-channel Bussgang blind deconvolution using expectation maximization (EM) algorithm and feedback

A Bussgang based blind deconvolution algorithm called self-correcting multi-channel Bussgang (SCMB) blind deconvolution algorithm was proposed. Unlike the original Bussgang blind deconvolution algorithm where the probability density function (pdf) of the signal being recovered is assumed to be completely known, the proposed SCMB blind deconvolution algorithm relaxes this restriction by parameterized the pdf with a Gaussian mixture model and expectation maximization (EM) algorithm, an iterative maximum likelihood approach, is employed to estimate the parameter side by side with the estimation of the equalization filters of the original Bussgang blind deconvolution algorithm. A feedback loop is also designed to compensate the effect of the parameter estimation error on the estimation of the equalization filters. Application of the SCMB blind deconvolution framework for binary image restoration, multi-pass synthetic aperture radar (SAR) autofocus and inverse synthetic aperture radar (ISAR) autofocus are exploited with great results.Ph.D.Committee Chair: Dr. Russell Mersereau; Committee Member: Dr. Doug Willams; Committee Member: Dr. Mark Richards; Committee Member: Dr. Xiaoming Huo; Committee Member: Dr. Ye (Geoffrey) L

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

Motion Measurement Errors and Autofocus in Bistatic SAR. Submitted to

This paper discusses the effect of motion measurement errors (MMEs) on measured bistatic synthetic aperture radar (SAR) phase history data that has been motion compensated to the scene origin. We characterize the effect of low frequency MMEs on bistatic SAR images, and based on this characterization, we derive limits on the allowable MMEs to be used as system specifications. Finally, we demonstrate that proper orientation of a bistatic SAR image during the image formation process allows application of monostatic SAR autofocus algorithms in post-processing to mitigate image defocus. Index Terms bistatic, SAR, motion measurement errors, ground map, autofocus