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.
Toeplitz Structured Covariance Matrix Estimation for Radar Applications / Du, X.; Aubry, A.; De Maio, A.; Cui, G.. - In: IEEE SIGNAL PROCESSING LETTERS. - ISSN 1070-9908. - 27:(2020), pp. 595-599. [10.1109/LSP.2020.2984431]
Toeplitz Structured Covariance Matrix Estimation for Radar Applications
Aubry A.;De Maio A.;
2020
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
