Efficient organization of large ship radar databases using wavelets and structured vector quantization

We investigate the problem of efficient representations of large databases of pulsed radar returns from naval vessels in order to economize memory and minimize search time. We use synthetic radar returns from ships as the experimental data. The results extend to real ISAR returns. We 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 TSVQ design algorithm is of the “greedy” type. Our experiments to date 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 efficient indexing scheme (with respect to variations in aspect, elevation and pulsewidth) for radar data which can facilitate the development ATR, surveillance and multi-sensor fusion systems