Wavelet Based Progressive Classification of High Range Resolution Radar Returns

We investigate the problem of fast and accurate classification of high range resolution radar returns from ships. In addition, we investigate the problem of efficient organization of large databases of pulsed high resolution radar returns from multiple targets in order to economize memory requirements and minimize search time. We use synthetic radar returns from ships as the experimental data. We develop a novel algorithm for hierarchically organizing the database which utilizes a multiresolution wavelet representation working in synergy with a Tree Structured Vector Quantizer (TSVQ), utilized in its clustering mode. The tree structure is induced by the multiresolution decomposition of the radar returns. The TSVQ design algorithm is of the “greedy” type. We demonstrate that our algorithm automatically computes the aspect graph (i.e. the simultaneous representation of compressed pulses as functions of aspect and elevation) for a single target or for a group of targets. We also develop a novel optimization framework for the simultaneous design of the wavelet basis, the Tree-Structured Vector Quantizer and the Classification rule. We describe an efficient and promising implementation consisting of an adaptive Wavelet Transform – Tree Structured Vector Quantization with Learning. We show experimental results which indicate that the combined algorithm executes orders of magnitude faster data search time, with negligible performance degradation (as measured by rate-distortion curves).