Knowledge-based adaptive polarimetric detection in heterogeneous clutter

The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based (KB) adaptive polarimetric detector in heterogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.