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W e investigate the problem of eficient representations of large databases of pulsed radar returns from naval vessels in order t o economize memory and minimize search time. W e use synthetic radar returns from ships as the experimental data. The results extend t o real I S A R retums. W e develop a novel algorithm for 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 pulses. The T S V Q design algorithm is of the "greedy" type. O u r experiments todate indicate that the combined algorithm results in orders of magnitude faster data search time, with negligible performance degradation from the full search vector quantization. The combined algorithm provides an eficient indexing scheme spect to variations in aspect, elevation and pu sewidth) for radar data which can facilitate the devlopment of A T R , surveillance and multi-sensor fusion systems.

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 ofthe "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).

ISR develops, applies and teaches advanced methodologies of design and analysis to ABSTRACTWe present a method for constructing efficient hierarchical organization of image databases for fast recognition and classification. The method combines a wavelet preprocessor with a Tree-Structured-Vector-Quantization for clustering. We show results of application of the method to ISAR data from ships and to face recognition based on photograph databases. In the ISAR case we show how the method constructs a multi-resolution aspect graph for each target.

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