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Parametric GLRT for multichannel adaptive signal detection in space–time correlated compound‐Gaussian disturbance

This study considers the problem of adaptive detection for a multichannel signal in the presence of spatially and temporally correlated compound-Gaussian disturbance. By modelling the disturbance as a multichannel autoregressive process, the authors resort to a parametric generalised likelihood ratio test (PGLRT) with the capability of dimension reduction. In the derivation, due to treating the random texture components of disturbance as deterministic unknown parameters, the test requires no a priori knowledge about the disturbance statistics. Moreover, the involving highly non-linear maximum-likelihood (ML) estimation procedure underlying the PGLRT is subtly addressed by ad hoc
 ML estimators. Accordingly, the derived detector applies to challenging cases with limited or even no training signals for parameter estimation. Such cases (especially when training-free case) are attractive for signal detection in heterogeneous or fast changing environments. In simulations, the performance evaluations are conducted by means of Monte Carlo trials. Numerical results investigate the issues of the estimator efficiency, detection and false alarm probabilities in terms of several concerned cases. Compared to the existing and benchmark detectors, the proposed PGLRT achieves improved detection performance, in general.