Neural network scheme for adaptive radar detection based on nonparametric statistics

Farid Amoozegar; Ali Notash; Seyed Mohammad Reza Sadat Hosseini

doi:10.1117/12.216958

18 August 1995 Neural network scheme for adaptive radar detection based on nonparametric statistics

Farid Amoozegar, Ali Notash, Seyed Mohammad Reza Sadat Hosseini

Proceedings Volume 2562, Radar/Ladar Processing and Applications; (1995) https://doi.org/10.1117/12.216958
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

Abstract

In this paper, we introduce a neural network (NN) architecture that utilizes nonparametric as well as the conventional parametric statistics. Use of the Wilcoxon two-sample test along with the classical model (e.g. Gaussian) parameters provide a qualitative as well as a quantitative representation of the target and the background. On an ordinal scale the radar returns from the target background are ranked according to a specified order and the neural network is trained with a qualitative factor for deviation from the normal distribution. In addition, the actual background distribution also depends on the type of the sensor as well as the wavelength of operation. Accordingly, the independence of the neural network training from the background noise and the clutter distribution provides a unified design approach for the microwave and the laser radar detection systems.

Citation Download Citation

Farid Amoozegar, Ali Notash, and Seyed Mohammad Reza Sadat Hosseini "Neural network scheme for adaptive radar detection based on nonparametric statistics", Proc. SPIE 2562, Radar/Ladar Processing and Applications, (18 August 1995); https://doi.org/10.1117/12.216958

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