Knowledge-Aided Structured Covariance Matrix Estimator Applied for Radar Sensor Signal Detection

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.

Training‐free moving target detection with uncertain a priori knowledge for airborne radar

“…The cell size is determined by the bandwidth and beamwidth of the airborne radar system. The detection is performed on each cell one by one to look for moving targets of interest [23, 24]. We assume that there are dominant clutter scattering points on the isorange.…”

Evolution of Netted Radar Systems

“…The reason behind this is that the CL-GLRT was derived assuming that u m,n,0 is an unknown deterministic number that could take on any value with equal probability, which is equivalent to ignoring the a priori knowledge regarding the pdf of u m,n,0 . Similarly, the TPCC-IG detector is shown to outperform the PCC-GLRT [96] and the KA-P-GLRT [97], which also jointly exploit the SCM, the persymmetric structure of Σ m,n , and the prior knowledge matrix.…”

“…into (3)), the KA-P-GLRT [97], the SCM-GLRT, and the CL-GLRT in partially homogeneous interference with u m,n,0 following the inverse Gamma distribution (β m,n = ρ m,n -1 = 9, μ m,n = 30) are plotted in FIGURE 4 assuming SINR = 10 dB. The parameters of the DMR used to generate these figures are the same with the ones used to generate FIGURE 3.…”

Distributed Target Detection in Unknown Interference

“…Multichannel signal detection is a fundamental problem in the signal processing community [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ]. Kelly first derived the generalized likelihood ratio test (GLRT) for multichannel radar systems in unknown noise in 1986 [ 9 ].…”