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.