Dual-Polarimetric Persymmetric Adaptive Subspace Detector for Range-Spread Targets in Heavy-Tailed Non-Gaussian Clutter
Xingyu Shi, Chaoqun Yang, Xiaofeng Wang, Shuoshuo Dong
Abstract
To solve the problem of range-spread targets detection in non-Gaussian clutter for dual-polarimetric radar systems, a novel dual-polarimetric persymmetric detector based on the two-step generalized likelihood ratio test (GLRT) is proposed. Firstly, the target signal is modeled as a multi-rank subspace signal, and the clutter is modeled as a compound Gaussian model with deterministic unknown texture. Then, by taking polarimetric and persymmetric characteristics of the clutter into account, the detection performance of the proposed detector is obviously improved. Finally, through the verification via both simulated data and measured data, it is shown that the proposed detector poses better detection performance in comparison with the existing non-polarimetric detectors. Due to the complexity of the detector form, the analytical expression of false alarm probability cannot be derived. Therefore, we verified the constant false alarm ratio (CFAR) characteristic of the proposed detector through simulation experiment.