Method for detecting weak signals in a non-gaussian and non-stationary

Abstract

A method is described for detecting weak signals in a non-Gaussian and non-stationary background using a hidden Markov parameter estimator. The method comprises the steps of: a) partitioning input data into sets of range bins, where the input data has a noise component; b) estimating hidden Markov model parameters from the input data; c) determining the average intensity I.sub.i of the range bins, where i represents an index for the ranges bins, and 1.ltoreq.i .ltoreq.R, and R represents the total number of range bins; d) estimating the average noise intensity N.sub.i of each range bin; e) determining the residual intensity estimates .rho..sub.i of each range bin as the maximum of 0 and (I.sub.i -N.sub.i) for all values of i; f) transforming the hidden Markov model parameters into estimates of hidden Markov model parameters of the noise component of the input data; g) generating modified locally optimal detection statistics representing the likelihood that the input data for the range bins contains a signal of interest using the estimates of the hidden Markov model parameters of the noise component of the input data, the residual intensity estimates, and the input data; h) transforming the modified locally optimal detection statistic into normalized values of the detection statistics applied to each range bin; and i) generating a detection output signal if any of the normalized values of the detection statistics exceeds a threshold.