This article investigates the dual-polarimetric target detection problem in non-Gaussian sea clutter. The compound Gaussian model with the inverse Gamma texture is adopted to describe the real sea clutter of which characteristics are time-varying and heterogeneous. Three novel polarimetric detectors are proposed based on the two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT), MAP Rao, and MAP Wald criteria. Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gamma textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we take advantage of the proposed polarimetric persymmetric property and MAP criterion to estimate the PCCM and inverse Gamma textures, respectively. The fully adaptive persymmetric polarimetric detectors are obtained by substituting the estimates into the test statistics of the proposed detectors. Then, the proposed detectors are proved to have constant false alarm rate (CFAR) properties with respect to the real PCCM. The performance assessments are evaluated by contrasting the proposed detectors with their counterparts on the simulated and measured sea clutter data. The numerical results verify the performance of the proposed persymmetric polarimetric detectors.

In this paper, a regression neural network is proposed instead of the commonly used classification neural network to address the direction of arrival (DOA) estimation problem. Unlike the method treating the DOA estimation problem as a classification task, our proposed method utilizes a regression neural network to directly estimate the signal angles by mapping the input data to the high-dimensional features. It is an end-to-end method where the input data is the signal covariance matrix estimated from the received signals by the uniform linear array. The network output consists of two branches: one branch provides the estimated angles of signals, and the other branch provides the confidences corresponding to angles, indicating the probabilities that signals are incident at the estimated angles. Owing to the dual-branch output of our proposed method, it is adept at accommodating a variable number of sources without the requirement to alter the network structure. Compared to classification neural networks, our proposed method circumvents the issue of quantization errors and has the capability to handle the imbalance of positive and negative samples. Simulation results validate the superior performance of our proposed method in terms of estimation success rate, angular resolution, and estimation accuracy, especially in scenarios with a variable and unknown number of coherent signals.

This letter addresses the persymmetric adaptive detection problem of range-spread targets in compound Gaussian sea clutter. Based on the generalized likelihood ratio test (GLRT), Rao test, and Wald test, three novel compound Gaussian detectors are developed. Specifically, the sea clutter is modeled as compound Gaussian with inverse Gaussian distribution, and the detectors are derived by the two-step maximum a posterior (MAP) procedures. In the first step, we assume the clutter covariance matrix (CCM) and the inverse Gaussian texture are known and derive the proposed detectors’ test statistics. In the second step, we use the persymmetric property and MAP criterion to estimate the CCM and inverse Gaussian texture parameters. Then the proposed detectors are proved to be constant false alarm rate (CFAR) detectors with respect to the real CCM. The simulation experiments are conducted by comparing the proposed detectors with their counterparts on both synthetic data and real sea clutter data. The numerical results indicate that the novel detectors exhibit better detection performance than their competitors.

This letter deals with the adaptive detection problem in compound Gaussian sea clutter with inverse Gaussian texture when the mismatched signal occurs. In order to reject the mismatched signal, we introduce a fictitious signal to the null hypothesis based on the adaptive beamformer orthogonal rejection test (ABORT). We adopt the two-step generalized likelihood ratio test (GLRT) criterion and develop two selective detectors, i.e., the ABORT-like compound Gaussian with inverse Gaussian (A-IGCG) detector and ABORT-like maximum a posteriori (MAP) compound Gaussian with inverse Gaussian (AM-IGCG) detector. Both the proposed detectors are proved to be constant false alarm rate (CFAR) detectors. The several experiments that compare the proposed detectors with other selective detectors and compound Gaussian detectors are conducted by using two types of real sea data, i.e., IPIX 1998 and 2019 real sea data. The numerical results indicate that the novel detectors exhibit significant tradeoffs between performances of detection and selectivity property.

This letter addresses the polarimetric target detection problem in compound Gaussian sea clutter. In view of the heavy-tailed characteristic of sea clutter, we model the sea clutter as compound Gaussian distribution with inverse Gaussian texture (IGCG). We propose three polarimetric compound Gaussian detectors based on the two-step Rao test, the Wald test, and the generalized likelihood ratio test (GLRT). Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gaussian textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we use the training data to estimate PCCM and maximum a posteriori (MAP) to estimate the inverse Gaussian textures, and we obtain the fully adaptive detectors. Then, we give proof of the constant false alarm rate (CFAR) properties of the proposed detectors. We validate the performance of the proposed polarimetric detectors by conducting experiments based on simulated and real sea clutter data. Finally, the numerical results indicate that the proposed detectors exhibit better detection performance than their competitors and are robust when the mismatched signal occurs.