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6 July 1994 Radar target discrimination using wavelet transforms
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rajan Varad "Radar target discrimination using wavelet transforms", Proc. SPIE 2235, Signal and Data Processing of Small Targets 1994, (6 July 1994);

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