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Proceedings Paper

Radar target discrimination using wavelet transforms
Author(s): Rajan Varad
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Paper Abstract

In this paper, we describe a new approach to radar target discrimination. Specifically, we will apply it to the problem of exo-atmospheric object discrimination from UHF radar returns. The method uses wavelet transforms, pattern recognition techniques such as feature spaces, vectors and neural net classifiers. Feature vectors for each object are constructed from the wavelet transforms of the input data samples. The feature vectors are based on energies at each scale of the wavelet transforms and therefore effectively circumvent the problem of noncoherence due to target and ionospheric effects. This is a very important consideration when coherent signal processing is not feasible. The feature vectors are input to an unsupervised learning neural network for classification of the objects. In unsupervised learning, the network output is not forced towards a desired response for each input pattern but allowed to learn proximity to past input patterns. Limited results from simulated radar cross-section (RCS) data indicate that most objects can be correctly classified. The results also show that the overall scheme is quite immune to fair amounts of gaussian as well as uniformly distributed noise. Further efforts are under way to test the methodology against real object data as well as more extensive simulations.

Paper Details

Date Published: 6 July 1994
PDF: 10 pages
Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994); doi: 10.1117/12.179092
Show Author Affiliations
Rajan Varad, MITRE Corp. (United States)


Published in SPIE Proceedings Vol. 2235:
Signal and Data Processing of Small Targets 1994
Oliver E. Drummond, Editor(s)

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