Statistical Pattern Recognition for Synthetic Aperture Radar (SAR)/ automatic Target Recognition (ATR). Volume 2

摘要

State-of-the-art research on spectral estimation, feature extraction, and pattern recognition algorithms are presented for radar signal processing and automatic target recognition. Advanced space-time spectral estimation algorithms are presented for multiple moving target feature extraction as well as clutter and jamming suppression for airborne high range resolution (HRR) phased-array radar. A nonparametric adaptive filtering-based approach, referred to as the Gapped-data Amplitude and Phase EStimation (GAPES) algorithm, is proposed for the spectral analysis of gapped data sequences as well as synthetic aperture radar (SAR) imaging with angle diversity data fusion. A QUasi-parametric ALgorithm for target feature Extraction (QUALE) algorithm is also investigated for angle diversity data fusion. Support Vector Machines (SVMs) as compared with other advanced classifiers in the MSTAR Public Domain Release and HRR data are found to outperform neural networks and matched filters. A new concept to create negative examples from the known target class is presented and shown to tremendously improve the rejection of confusers. Finally, Information Theoretic Learning (ITL) is proposed as a new algorithm to demix HRR signatures of closely parked targets.