Title :
Neural networks for sequential discrimination of radar targets
Author :
Haimerl, Joseph A. ; Geraniotis, Evaggelos
Author_Institution :
General Electric Co., Moorestown, NJ, USA
Abstract :
Perceptron neural networks are applied to the problem of discriminating between two classes of radar returns. The perceptron neural networks are used as nonlinearities in two threshold sequential discriminators which act upon samples of the radar return. The neural network´s training phase eliminates the impractical task of estimating high-order probability density functions when designing a discriminator; consequently discriminators with memory are easily obtained. The discriminators using neural networks for their nonlinearities significantly outperform the optimal memoryless discriminators of Geraniotis (1989). The discriminators constructed with neural networks made no classification errors in 10000 trials from each hypothesis. These discriminators also used a significantly smaller expected number of samples to make their decisions than did known discriminators
Keywords :
discriminators; neural nets; radar cross-sections; memory; nonlinearities; perception neural networks; radar returns; radar targets; sequential discrimination; threshold sequential discriminators; training phase; Design optimization; Educational institutions; Laboratories; Minimax techniques; Neural networks; Probability density function; Radar; Sequential analysis; Statistical analysis; Testing;
Conference_Titel :
Radar Conference, 1991., Proceedings of the 1991 IEEE National
Conference_Location :
Los Angeles, CA
Print_ISBN :
0-87942-629-2
DOI :
10.1109/NRC.1991.114737