Toeplitz Structured Covariance Matrix Estimation for Radar Applications
Xiaolin Du, Augusto Aubry, Antonio De Maio, Guolong Cui
Abstract
Following a geometric paradigm, the estimation of a Toeplitz structured covariance matrix is considered. The estimator minimizes the distance from the Sample Covariance Matrix (SCM) while complying with some specific constraints modeling the covariance structure. The resulting constrained optimization problem is solved globally resorting to the Dykstra' projection framework. Each step of the procedure involves the solution of two convex sub-problems, whose minimizers are available in closed form. Simulation results related to typical radar environments highlight the effectiveness of the devised method.
Topics & Concepts
Toeplitz matrixCovariance matrixEstimation of covariance matricesCovarianceCovariance intersectionMathematical optimizationEstimatorConvex optimizationRadarCMA-ESAlgorithmMathematicsRational quadratic covariance functionMatrix (chemical analysis)Covariance functionComputer scienceLaw of total covarianceApplied mathematicsRegular polygonStatisticsTelecommunicationsComposite materialGeometryPure mathematicsMaterials scienceRadar Systems and Signal ProcessingDirection-of-Arrival Estimation TechniquesTarget Tracking and Data Fusion in Sensor Networks