Performance analysis of a detector for nonstationary random signals

Abstract

The detection of nonstationary random signals is an important sonar problem which also has potential applications in diverse areas such as biomedical signal processing and spread spectrum communications. The primary problem with applying a powerful test like the generalized likelihood ratio test (GLRT) is the computational effort required to search for the maximum likelihood model parameters for the observed signal. The computation required is multiplied many times over when a signal parameter is nonstationary. A computationally efficient detector of nonstationary Gaussian random signals based on the GLRT was presented at ICASSP94 [1]. A slightly enhanced version of the detector is described below, along with new simulation results demonstrating that the detector performs nearly optimally and is quite robust to signal model inaccuracy.