Adaptive Persymmetric Detection for Radar Targets in Correlated CG-LN Sea Clutter

This article deals with the detection problem of a moving point-like target in correlated non-Gaussian sea clutter, which is modeled by a compound Gaussian model with a lognormal-distributed texture and an unknown covariance matrix. To improve the detection performance for radar targets in sample-starved environments where the number of secondary data is limited, the persymmetric structure is exploited to transform the original radar data. Based on the two-step generalized likelihood ratio test (GLRT) and its maximum posterior version, we propose two adaptive persymmetric coherent detectors for radar target detection. Theoretical and experimental confirmations are provided to show that the proposed detectors guarantee the constant false alarm rate (CFAR) property with respect to the clutter covariance matrix structure and the clutter power mean. Experimental results on simulated and measured radar data demonstrate that two proposed detectors perform better than traditional ones, especially when the number of secondary data is small.