Adaptive locally optimal detection using RBF neural network

This paper addresses the application of locally optimum (LO) signal detection techniques to environments in which the noise density is not known a priori. For small signal levels, the LO detection rule is shown to involve a nonlinearity which depends on the noise density. The estimation of the noise density is a major part of the computational burden of LO detection rules. In this paper, adaptive estimation of the noise density is implemented using a radial basis function (RBF) neural network. Unlike existing algorithms, the present technique places few assumptions on the properties of the noise, and performs well under a wide variety of circumstances. Experimental results are shown which illustrate the system performance as a variety of noise densities are encountered. >