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13 May 2019 Automatic target recognition using non-negative matrix approximations (Conference Presentation)
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Abstract
The set of orthogonal Eigen-vectors built via principal component analysis (PCA), while very effective for compression, can often lead to loss of crucial discriminative information in signals. In this work, we build a basis set using non-negative matrix approximations (NNMAs). We are interested in testing radar data with the non-negative basis and an accompanying non-negative coefficient set in order to understand if the NNMAs is able to produce a more accurate generative model than the PCA the PCA basis which lacks direct physical interpretation. It is hoped that the NNMA basis vectors, while not orthogonal, capture discriminative local components of targets in processed radar data. We test the merits of the NNMA basis representation for the problem of automatic target recognition. Experiments on synthetic radar data are performed and the results are discussed.
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Vahid R. Riasati "Automatic target recognition using non-negative matrix approximations (Conference Presentation)", Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 109950A (13 May 2019); https://doi.org/10.1117/12.2519225
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