Title :
Neural network based optimum radar target detection in non-Gaussian noise
Author :
Kim, Moon W. ; Arozullah, Mohammed
Author_Institution :
US Naval Res. Lab., Washington, DC, USA
Abstract :
The application of neural networks to radar target detection in non-Gaussian noise environments is investigated. Two new probabilistic neural networks, the Gram-Charlier neural network and the Gram-Charlier probabilistic neural network, were applied to the radar detection. The performance of these detectors was evaluated and compared with backpropagation and Bayesian classifiers by simulation for Gaussian, Weibull, and lognormal noise environments
Keywords :
neural nets; pattern recognition; radar applications; Bayesian classifiers; Gaussian; Gram-Charlier neural network; Gram-Charlier probabilistic neural network; Weibull; backpropagation; lognormal noise environments; neural network based optimum radar target detection; nonGaussian noise; Backpropagation; Bayesian methods; Detectors; Doppler radar; Intelligent networks; Maximum likelihood detection; Neural networks; Object detection; Radar detection; Working environment noise;
Conference_Titel :
Neural Networks, 1992. IJCNN., International Joint Conference on
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-0559-0
DOI :
10.1109/IJCNN.1992.227118
