Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
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By K.-C. Lee, J.-S. Ou, and M.-C. Fang

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The noise effect is very challenging in radar target recognition. It usually degrades the accuracy of target recognition and then makes the recognition unreliable. In this study, we present a noise-reduction technique to improve the accuracy of radar target recognition. Our noise-reduction technique is based on the SVD (singular value decomposition). The PCA (principal components analysis) based radar recognition algorithm is utilized to verify our noise-reduction scheme. In our treatment, the received signals are arranged into a Hankel-form matrix. This Hankel-form matrix is decomposed into two subspaces, i.e., the noise-related subspace and clean-signal subspace. The noise reduction is obtained by suppressing the noise-related subspace and retaining the clean-signal space only. Simulation results show that the accuracy of target recognition is greatly improved as the received signals are first processed by the SVD noise-reduction technique. With the use of proposed noise-reduction scheme, the radar target recognition can tolerate more noises and then becomes more reliable. The noise-reduction technique in this study can also be applied to many other problems in radar engineering.

K.-C. Lee, J.-S. Ou, and M.-C. Fang, "Application of Svd Noise-Reduction Technique to PCA Based Radar Target Recognition," Progress In Electromagnetics Research, Vol. 81, 447-459, 2008.

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