Adaptive CFAR Rao and Wald Detectors for Compound Gaussian Sea Clutter with Inverse Gaussian Texture

Abstract

This paper deals with the adaptive target detection in compound Gaussian sea clutter with inverse Gaussian texture. The detectors based on two-step Rao and Wald criteria are derived, and both of the proposed detectors possess the property of constant false alarm rate (CFAR). Specifically, we assume that the texture parameter and clutter covariance matrix are known and derive the Rao and Wald tests' test statistics in the first step. Then we utilize the maximum a posteriori (MAP) method and fixed point covariance estimator (FPCE) to estimate the parameter of texture and clutter covariance matrix, respectively. Finally, we conduct the simulation experiments by comparing with its counterpart generalized likelihood ratio test (GLRT) using simulated data and real sea clutter data, i.e., IPIX 1998. The performance assessment results indicate that the proposed Rao detector outperforms the GLRT, and the proposed Wald detector holds better selectivity than GLRT.