The non-Gaussian nature of radar-observed clutter echoes induces performance degradation in the context of remote sensing target detection when using conventional Gaussian detectors. To enhance target detection performance, this study addresses the issue of adaptive detection in nonzero-mean non-Gaussian sea clutter environments. The nonzero-mean compound Gaussian model, composed of the texture and complex Gaussian speckle, is utilized to capture the sea clutter. Further, we adopt the inverse Gamma, Gamma, and inverse Gaussian distributions to characterize the texture component. Novel adaptive detectors based on the two-step Rao and Wald tests, taking advantage of the maximum a posteriori (MAP) method to estimate textures, are designed. More specifically, test statistics of the proposed Rao- and Wald-based detectors are derived by assuming the speckle covariance matrix (CM), mean vector (MV), and clutter texture in the first step. Then, the sea clutter parameters assumed to be known are replaced with their estimations, and fully adaptive detectors are obtained. The Monte Carlo performance evaluation experiments using both simulated and measured sea clutter data are conducted, and numerical results validate the constant false alarm rate (CFAR) properties and detection performance of the proposed nonzero-mean detectors. Additionally, the proposed Rao and Wald detectors, respectively, show strong robustness and good selectivity for mismatch signals.

This letter deals with the target detection problem for polarimetric marine radar. The sea clutter is modelled as the compound Gaussian (CG) distribution with lognormal texture. We propose three detectors based on the two-step generalized likelihood ratio test (GLRT), the complex value Rao, and Wald tests by utilizing the persymmetric properties of the polarimetric speckle covariance matrix (CM). The lognormal texture component and the speckle CM are estimated by the maximum a posteriori (MAP) criterion and the polarimetric persymmetric fixed-point estimator, respectively. In addition, we provide proof of the constant false alarm rate (CFAR) properties of the designed polarimetric detectors. Moreover, we evaluate the detection performance of the proposed detectors in the simulated data and the measured sea clutter data, and the simulation results show the proposed detector outperforms the competitors more than 1dB in different situations.

This article investigates the adaptive radar detection problem in a nonzero-mean background, where both the covariance matrix (CM) and mean vector (MV) of interference signals remain unknown. Utilizing the Rao (one-step, modified one-step, and two-step) and Wald (one-step and two-step) tests, fully adaptive nonzero-mean detectors are developed to approach the detection problem. Under the nonzero-mean circumstances, we present derivations of the Rao and Wald tests where parameters include the unknown MV. To be specific, one-step detectors exhibit native adaptivity, whereas the test statistics derived from two-step criteria require inserting MV and CM estimates to achieve fully adaptive detection. The analytical expressions for the probability of false alarm (Pfa) are obtained, and they show that the proposed detectors hold the constant false alarm rate (CFAR) property against the MV and CM. Moreover, the theoretical expressions for the detection probability (Pd) are derived in both matched and mismatched cases to assess the performance of the devised nonzero-mean detectors. Numerical examples illustrate that the developed Rao- and Wald-based detectors enjoy good capabilities in terms of the selectivity and robustness against the mismatch.

This article addresses the detection problem in the situation of target energy spillover in compound Gaussian clutter. The clutter is described as the multiplicative combination of log normal modeled texture and complex Gaussian distributed speckle component. By utilizing the two-step generalized likelihood ratio test (GLRT), the Rao, and gradient tests, four detectors are developed, where the estimated speckle covariance matrix is substituted into the test statistics yielded in the first step. The first GLRT-based detector estimates the texture component by iterative solution in the alternative hypothesis, and the second GLRT-based detector estimates the texture by utilizing the bisection method. Further, the constant false alarm rate (CFAR) properties analysis as well as the complexity analysis of the derived detectors are provided. The simulation experiments demonstrate the proposed detectors achieve superior performance compared to the conventional detectors.