Wavelet-based progressive classification of high-range resolution radar returns

John S. Baras; Sheldon I. Wolk

doi:10.1117/12.170034

15 March 1994 Wavelet-based progressive classification of high-range-resolution radar returns

John S. Baras, Sheldon I. Wolk

Proceedings Volume 2242, Wavelet Applications; (1994) https://doi.org/10.1117/12.170034
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

In this paper we investigate the problem of fast and accurate classification of high range resolution radar returns. In addition we investigate the problem of efficient organization of large databases of pulsed high resolution radar returns from naval vessels in order to economize memory requirements and minimize search time. We use both synthetic radar returns from ships as well as real ISAR returns 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 pulses. 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 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 show that an efficient implementation consists of an adaptive Wavelet Transform - Tree Structured Vector Quantization with Learning. We show experimental results on the performance of the algorithm as measured by: (a) memory requirements; (b) search time; (c) scaling with respect to size; (d) accuracy in recovery. We also show experimental results with respect to variations in the mother wavelet and the design of the tree, as well as their impact on the performance of the algorithm. The results indicate that the combined algorithm results in orders of magnitude faster data search time, with negligible performance degradation (as measured by rate-distortion curves).

Citation Download Citation

John S. Baras and Sheldon I. Wolk "Wavelet-based progressive classification of high-range-resolution radar returns", Proc. SPIE 2242, Wavelet Applications, (15 March 1994); https://doi.org/10.1117/12.170034

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available