WAVENET feature extraction of high-range resolution radar profiles for automatic target recognition

Hedley C. Morris; Monica M. De Pass

doi:10.1117/12.603976

28 March 2005 WAVENET feature extraction of high-range resolution radar profiles for automatic target recognition

Hedley C. Morris, Monica M. De Pass

Proceedings Volume 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III; (2005) https://doi.org/10.1117/12.603976
Event: Defense and Security, 2005, Orlando, Florida, United States

Abstract

We propose a WAVENET method for feature extraction of high-range resolution (HRR) radar profiles. Because HRR signals constantly vary with incremental changes in time and target aspect, the inverse problem we address is that of extracting a subset of discriminatory features from a set of HRR profiles that are unique to each target class. Based on, we construct a neural net technique built on wavelets for determining the discriminating features separating each target class. The method involves choosing a suitable set of child wavelets, such that the transformation of the original data (the training set of HRR profiles) will enhance the nonlinear separability of different classes of target signals while significantly reducing the dimension of the data.

Citation Download Citation

Hedley C. Morris and Monica M. De Pass "WAVENET feature extraction of high-range resolution radar profiles for automatic target recognition", Proc. SPIE 5818, Independent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks III, (28 March 2005); https://doi.org/10.1117/12.603976

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