This study deals with the problem of covariance matrix estimation for radar sensor signal detection applications with insufficient secondary data in non-Gaussian clutter. According to the Euclidean mean, the authors combined an available prior covariance matrix with the persymmetric structure covariance estimator, symmetric structure covariance estimator, and Toeplitz structure covariance estimator, respectively, to derive three knowledge-aided structured covariance estimators. At the analysis stage, the authors assess the performance of the proposed estimators in estimation accuracy and detection probability. The analysis is conducted both on the simulated data and real sea clutter data collected by the IPIX radar sensor system. The results show that the knowledge-aided Toeplitz structure covariance estimator (KA-T) has the best performance both in estimation and detection, and the knowledge-aided persymmetric structure covariance estimator (KA-P) has similar performance with the knowledge-aided symmetric structure covariance estimator (KA-S). Moreover, compared with existing knowledge-aided estimator, the proposed estimators can obtain better performance when secondary data are insufficient.

In this paper, we address the problem of clutter covariance matrix estimation for radar adaptive detection. Traditional estimation methods are usually based on specific models. However, performance will experience degradation in the presence of model mismatch, which occurs commonly in reality. Therefore, we resort to the data-driven deep learning method and construct a network based on the convolutional neural network (CNN) to estimate the clutter covariance matrix. Besides, due to the unavailable ground truth of covariance matrix of measured data, simulated data are usually applied for training as a compromise. We design a loss function according to whitening metric, which makes it possible to train the network directly by measured data. Compared with traditional covariance matrix estimators, the proposed network estimator has higher whitening ability. Moreover, we exploit the obtained covariance matrix estimations to an adaptive detector to evaluate the detection performance. Results with the IPIX datasets show that the detector applying the network covariance matrix estimator gains higher probability of detection (PD).

In this paper, we address the problem of polarimetric detection of a target with energy spillover in Gaussian environment. We adopt full polarized channels and use diverse criteria to design detectors, resulting in five new adaptive detectors. Performance evaluation reveals that the derived detectors gain higher probabilities of detection (PD) and better localization abilities, compared to the detectors which only use single-channel or dual-channel polarized data. A knowledge-aided detector is also proposed to give a further boost in detection and localization performances, especially under small number of secondary data. Besides, all the derived detectors guarantee the bounded constant false alarm rate (CFAR) properties. Experiments with real data from IPIX radar validate the superior performance and the practicability of the proposed detectors.

In this paper, we propose a radar adaptive detection framework that leverages persymmetric prior knowledge (PPK) of the clutter covariance matrix (CCM) to improve both the accuracy of environment model recognition and the performance of target detection. The framework first employs the Akaike information criterion (AIC), a model order selection (MOS) technique, to identify the optimal environmental model. It then selects the most suitable persymmetric adaptive detector for target detection based on the identified model. Compared to conventional methods that do not utilize PPK, our approach demonstrates superior performance in both environmental recognition and target detection.