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Journal of Applied Sciences
Year: 2010  |  Volume: 10  |  Issue: 13  |  Page No.: 1331 - 1335

Rotated Kernel Neural Networks for Radar Target Detection in Background Noise

A. Lotfi, K. Mezzoug and A. Benyettou    

Abstract: This study presents the principle of operation of the Rotated Kernel Neural Network (RKNN) for radar target detection in non-Gaussian noise. This classifier is based on adopting the architecture of standard probabilistic neural networks and using different kernel functions to approximate density functions. The training algorithm for this classifier is more complicated than the original PNN training algorithm but allow better generalization. Performance curves of the Rotated Kernel Neural Network are compared to those of probabilistic neural networks (original), Radial Basis Neural Networks with an expectation maximization training algorithm and Back propagation neural networks for Radar target detection in background noise in terms of probability of detection versus signal-to-noise ratio (SNR). For most cases, the Rotated Kernel Neural Network classifier outperforms other conventional Radar target detection techniques and presents the advantage of resistance to background noise for values of SNR greater than 5 dB.

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