Abstract
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.
Highlights
Covariance matrix estimation plays an important role in advanced radar sensor signal processing algorithms such as space–time adaptive processing (STAP) [1] and adaptive signal detection [2,3,4,5,6]
We use the adaptive normalized matched filter (ANMF) detector with the knowledge-aided persymmetric structure covariance estimator (KA-P), knowledge-aided symmetric structure covariance estimator (KA-S), knowledge-aided Toeplitz structure covariance estimator (KA-T), sample covariance matrix (SCM), convex combination (CC) and maximum likelihood (ML) estimators with one set of real data collected by the McMaster IPIX radar sensor system
We have considered the problem of covariance estimation with insufficient secondary data in compound-Gaussian clutter
Summary
Covariance matrix estimation plays an important role in advanced radar sensor signal processing algorithms such as space–time adaptive processing (STAP) [1] and adaptive signal detection [2,3,4,5,6]. In [24], the authors used a maximum likelihood (ML) estimator approach to obtain the optimal weighting factor via a search method in the Gaussian environment. Existing KA methods can solve the sample-starvation problem but they will suffer from loss due to model mismatch when the clutter is non-Gaussian [40,41,42] Another class of color-loaded methods use the Bayes framework to obtain covariance matrix estimation. We show that the proposed estimators can use knowledge effectively and have a better estimation and detection performance both in Gaussian and non-Gaussian clutter when secondary data are insufficient These results are valiadted by numerical experiments and real sea clutter data collected by the IPIX radar sensor system.
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