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Adaptive detection of small sinusoidal signals in non-Gaussian noise using an RBF neural network

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3 Author(s)
D. M. Hummels ; Dept. of Electr. & Comput. Eng., Maine Univ., Orono, ME, USA ; W. Ahmed ; M. T. Musavi

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

Published in:

IEEE Transactions on Neural Networks  (Volume:6 ,  Issue: 1 )