To use moving target signatures in synthetic aperture radar (SAR) for classification and identification, it is desirable to obtain a well-focused image from the blurred moving target signature. While a substantial amount of research has been done on solving the constant chirp rate defocusing of a moving target, this often leaves significant blurring in the moving target signature of a real target. This work aims at improving previous work on autofocus, specifically considering higher order motion terms, including those due to range walk and small rotations. Additionally, we aim to reduce the reliance on bright well-separated point scatterers. We apply SAR autofocus methods normally used for full imagery to moving targets: minimum entropy and phased gradient autofocus, and include recent innovations that allow these methods to handle range walk. Finally, we add a novel range-varying autofocus stage. We use SAR imagery of a truthed moving pickup truck collected with the MIT Airborne Radar Testbed (ARTB) to compare autofocus algorithms.

Detecting moving targets in synthetic aperture radar (SAR) imagery has recently gained a lot of interest as a way to augment optical moving target detection and classification in adverse (e.g., cloudy) weather conditions. In this paper, we primarily focus on the problem of detecting and imaging moving targets in single-channel (or summed multichannel) SAR data. Single-channel-based methods provide the ability to do detection well below the normal minimum detectable velocity (MDV) of multichannel-based GMTI. This can be particularly important for small radar antennas, which tend to have high conventional GMTI MDV. We also show results for multiple channel geolocation after single-channel detection and imaging. The algorithms consist of the following steps. We first suppress the stationary scene by comparing noncoherent time-subimages. We then detect the movers by applying a set of possible motion corrections to the image and use a novel matched filter to detect the movers in this space. We can then image the moving targets using standard SAR focusing techniques and geolocate the movers using multichannel (if available) along-track interferometry. We demonstrate and evaluate our algorithms using data collected from the Lincoln Multimission ISR Testbed airborne radar system.

In many cases, the maximum likelihood (ML) estimator is consistent and asymptotically normal with covariance equal to the inverse of the Fisher's information matrix. It does not follow, though, that the covariance of the ML estimator approaches the Cramer-Rao lower bound as the sample size increases. However, it is possible to draw such a conclusion for the adaptive array problem in which direction of arrival and signal magnitude are being estimated. Proofs of w-asymptotic efficiency, which comes with a convergence-of-moments condition, and strong consistency (almost-sure convergence) of the ML estimator are given. Strong consistency is also proved for a popular quasi-ML estimator.>

An asymptotic analysis based on large deviation theory is presented for three representative nonlinear receivers operating in the presence of Gaussian noise, namely radiometers, differential phase shift keying, and memoryless detectors. En route to the results an extension to the Toeplitz distribution theorem is proved. This extension provides the key to obtaining closed-form expressions for the exponential rate of decrease of the probability of error for the first two detector structures. The solution to the rate constant problem for the memoryless detector is then shown to be given by the solution to a certain nonlinear Hammerstein integral equation. Several examples are presented.>

The asymptotic probability of error for quantization in maximum-likelihood tests is analyzed. The authors assume quantizers with large numbers of levels generated from a companding function. A theorem that relates the companding function to the asymptotic probability of error is proved. The companding function is then optimized.>