Detection of nonstationary random signals in colored noise

This paper decribes a novel method for detecting nonstationary signals in colored noise. A first order complex autoregressive, or AR(l), signal model is used which restricts the application of the detector to low order signals, i.e., those which are well modeled by a low order AR process and have only a single spectral peak. The detector assumes the noise covariance is stationary and known. The likelihood function is estimated in the frequency domain because the model simplifies, and the nonstationary frequency estimate can be obtained by an algorithm which approximates the Viterbi algorithm. The AR model parameters are then used to form the appropriate covariance matrix and the approximate likelihood is calculated. Therefore, the detector uses efficient approximations to approximate the generalized likelihood ratio test (GLRT). Simulation results are shown to compare the detector with the known signal likelihood ratio test.