title: Adaptive detection of small sinusoidal signals in non-Gaussian noise using an RBF neural network.
abstract: 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 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.
authors.affiliation: Dept. of Electr. and Comput. Eng., Maine Univ., Orono, ME.
authors.forename: D M
authors.initials: DM
authors.lastname: Hummels
authors.forename: W
authors.initials: W
authors.lastname: Ahmed
authors.forename: M T
authors.initials: MT
authors.lastname: Musavi