In this paper, we deal with the problem of estimating the disturbance covariance matrix for radar signal processing applications, when a limited number of training data is present. We determine the maximum likelihood (ML) estimator of the covariance matrix starting from a set of secondary data, assuming a special covariance structure (i.e., the sum of a positive semi-definite matrix plus a term proportional to the identity), and a condition number upper-bound constraint. We show that the formulated constrained optimization problem falls within the class of MAXDET problems and develop an efficient procedure for its solution in closed form. Remarkably, the computational complexity of the algorithm is of the same order as the eigenvalue decomposition of the sample covariance matrix. At the analysis stage, we assess the performance of the proposed algorithm in terms of achievable signal-to-interference-plus-noise ratio (SINR) both for a spatial and a Doppler processing. The results show that interesting SINR improvements, with respect to some existing covariance matrix estimation techniques, can be achieved. © 2012 IEEE.

Aubry, A., De Maio, A., Pallotta, L., Farina, A. (2012). Maximum likelihood estimation of a structured covariance matrix with a condition number constraint. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 60(6), 3004-3021 [10.1109/TSP.2012.2190408].

Maximum likelihood estimation of a structured covariance matrix with a condition number constraint

PALLOTTA, LUCA;
2012-01-01

Abstract

In this paper, we deal with the problem of estimating the disturbance covariance matrix for radar signal processing applications, when a limited number of training data is present. We determine the maximum likelihood (ML) estimator of the covariance matrix starting from a set of secondary data, assuming a special covariance structure (i.e., the sum of a positive semi-definite matrix plus a term proportional to the identity), and a condition number upper-bound constraint. We show that the formulated constrained optimization problem falls within the class of MAXDET problems and develop an efficient procedure for its solution in closed form. Remarkably, the computational complexity of the algorithm is of the same order as the eigenvalue decomposition of the sample covariance matrix. At the analysis stage, we assess the performance of the proposed algorithm in terms of achievable signal-to-interference-plus-noise ratio (SINR) both for a spatial and a Doppler processing. The results show that interesting SINR improvements, with respect to some existing covariance matrix estimation techniques, can be achieved. © 2012 IEEE.
2012
Aubry, A., De Maio, A., Pallotta, L., Farina, A. (2012). Maximum likelihood estimation of a structured covariance matrix with a condition number constraint. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 60(6), 3004-3021 [10.1109/TSP.2012.2190408].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/356058
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 121
  • ???jsp.display-item.citation.isi??? 111
social impact