DETECTION IN MULTIVARIATE NON-GAUSSIAN NOISE

The applications of multivariate Edgeworth series and higher-order sta tistics to the discrete-time detection of a known constant signal in m ultivariate non-Gaussian noise are considered. A technique to derive s uboptimum detectors from the Neyman-Pearson optimum and locally optimu m detectors is described. A numerical algorithm based on knowledge of the noise cumulants is presented in order to analyze the finite-sample size performance of the suboptimum detectors. As an example, the perf ormance of the detectors as compared with the linear detector in multi variate Gaussian-Gaussian mixture noise is presented via receiver oper ating characteristic curves. Numerical results indicate that the subop timum detectors, when exploiting knowledge of the dependence structure of the noise, can have very good performance with respect to the line ar detector.