DETECTION IN CORRELATED IMPULSIVE NOISE USING 4TH-ORDER CUMULANTS

In this work, we consider detection and estimation in correlated impul sive noise. The non-Gaussian impulsive noise is modeled as the sum of two linear processes: a nominal part and an impulsive part. This model admits correlated impulsive bursts lasting many data samples. Identif iability of the noise model is established using fourth- and second-or der cumulants. Under this model, the correlated lime series can be whi tened and an appropriate memoryless nonlinearity applied to attenuate the impulsive events. A detection statistic is then formed from the ou tput of the nonlinearity. In the threshold detection case, the use of cumulants allows identification of the noise in the presence of the si gnal to be detected, obviating the need for noise-only training record s. Simulation results with sample size of 512 show small loss in detec tor performance versus an ideal detector with no impulsive part presen t.