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.

Date Published: 18 August 1995
PDF: 12 pages
Proc. SPIE 2562, Radar/Ladar Processing and Applications, (18 August 1995); doi: 10.1117/12.216958

Published in SPIE Proceedings Vol. 2562:
Radar/Ladar Processing and Applications
William J. Miceli, Editor(s)