Generalised persymmetric parametric adaptive coherence estimator for multichannel adaptive signal detection

In this study, the authors deal with the problem of detecting a signal in partially homogeneous environments, where both the test data and the training data share the same covariance matrix up to an unknown scaling factor. A generalised persymmetric parametric adaptive coherence estimator (GPer-PACE) detector is proposed, where the disturbance is modelled as a multichannel autoregressive process. To mitigate the effect of limited training samples, the subspatial aperture smoothing is performed in the design of the authors’ GPer-PACE detector. Moreover, the persymmetric structure information is exploited to further reduce the sample requirements. The performance of the GPer-PACE is assessed by numerical examples. The results show that the GPer-PACE outperforms other traditional detectors in sample-deficient scenarios.