Unit 93046 of PLA, Qingdao 266111, China.
School of Electronic Science, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2019 Feb 6;19(3):664. doi: 10.3390/s19030664.
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.
本研究针对非高斯杂波环境下二次数据不足的雷达传感器信号检测应用中的协方差矩阵估计问题。根据欧几里得均值，作者分别将可用的先验协方差矩阵与对称结构协方差估计器、反对称结构协方差估计器和 Toeplitz 结构协方差估计器相结合，推导出三种知识辅助的结构协方差估计器。在分析阶段，作者评估了所提出的估计器在估计精度和检测概率方面的性能。分析基于模拟数据和由 IPIX 雷达传感器系统收集的实际海洋杂波数据进行。结果表明，知识辅助的 Toeplitz 结构协方差估计器（KA-T）在估计和检测方面都具有最佳性能，知识辅助的反对称结构协方差估计器（KA-P）与知识辅助的对称结构协方差估计器（KA-S）具有相似的性能。此外，与现有的知识辅助估计器相比，当二次数据不足时，所提出的估计器可以获得更好的性能。
