Absfracf -The asymptotic probability of error for quantization in maximum likelihood tests is analyzed. We assume quantizers with large numbers of levels generated from a companding function. A theorem which relates the companding function to the asymptotic probability of error is proven. The companding function is then optimized.
Adaptive High-Definition Imaging (HDI) is a data-adaptive approach to SAR image reconstruction based on superresolution techniques originally developed for passive sensor arrays. The problem at hand is the detection and recognition of ground-based targets in a clutter-dominated environment via UHF and L-Band foliage-penetrating SAR. Unfortunately, the resolution achieved in conventionally generated images (e.g., FFTtype imaging) is limited due to longer wavelengths and smaller bandwidths, as compared to high-resolution Xand Ka-Band SAR. A comparison of imaging techniques is presented, including conventional imaging, a 2-D technique based on the MLM (Capon) algorithm, and a 2-D version of the MUSIC algorithm. Results are presented for Wideband Rail SAR measurements of reflectors both in and out of foliage, demonstrating resolution improvement and clutter rejection. Also, results of processing data from an airborne wideband UHF SAR further demonstrate significant rejection of clutter which promises significant improvements in false-alarm performance.
INTROI)UCTIONWide-area surveillance for the detection and recognition of targets is a problem presenting significant technical challenges. A most demanding and critical problem is to achieve high search rates with limited false alanns for targets concealed in foliage or camouflage, at any time of day and in all weather. Wideband UHF SARs are of considerable interest because of their potential to penetrate foliage. However, UHF radar has the drawback of poor resolution, an obstacle which calls for innovative processing techniques.The goal of reliable Automatic Target Recognition (ATR) requires a significant reduction in false-alarm rates. Imaging techniques which provide more apparent resolution are of considerable interest because of the strong correspondence between resolution and ATR performance, i.e., having fewer pixels on the target implies poorer recognition. Also, techniques which exploit pixel phase and polarization, sidelobe structure and other yetto-be-discovered features, will be necessary to achieve the desired improvement. This report presents the results of a preliminary investigation into the feasibility of applying adaptive processing to SAR data. We refer to this class of techniques as Adaptive High-Definition Imaging (HDI), an approach to imaging based on modern spectrum-estimation techniques, also known as superresolution techniques, which have evidenced considerable operational success over the last several years. Significant improvements in resolution with passive arrays of sensors have been achieved by exploiting spatial separations as well as spectral, temporal, and polarimetric differences in the received signals [1]. It is precisely this exploitation of features in the data which HDI brings to SAR data processing.In this investigation, several algorithmic approaches developed initially for other applications have been modified to effect HDI. The techniques were tested on data collected for other experiments using radars which were not designed ...
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