Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Covariance matrix estimation for CFAR Detection in correlated heavy tailed clutter
Signal Processing - Signal processing with heavy-tailed models
BORD: Bayesian optimum radar detector
Signal Processing
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,
Maximum Likelihood Estimation of Compound-Gaussian Clutter and Target Parameters
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Statistical analysis of the nonhomogeneity detector for non-Gaussian interference backgrounds
IEEE Transactions on Signal Processing
Performance Analysis of Covariance Matrix Estimates in Impulsive Noise
IEEE Transactions on Signal Processing
Exact Maximum Likelihood Estimates for SIRV Covariance Matrix: Existence and Algorithm Analysis
IEEE Transactions on Signal Processing - Part I
Signal detection in Gaussian noise of unknown level: An invariance application
IEEE Transactions on Information Theory
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In the context of radar detection, the clutter covariance matrix estimation is an important point to design optimal detectors. While the Gaussian clutter case has been extensively studied, the new advances in radar technology show that non-Gaussian clutter models have to be considered. Among these models, the spherically invariant random vector modelling is particularly interesting since it includes the K-distributed clutter model, known to fit very well with experimental data. This is why recent results in the literature focus on this distribution. More precisely, the maximum likelihood estimator of a K-distributed clutter covariance matrix has already been derived. This paper proposes a complete statistical performance analysis of this estimator through its consistency and its unbiasedness at finite number of samples. Moreover, the closed-form expression of the true Cramer-Rao bound is derived for the K-distribution covariance matrix and the efficiency of the maximum likelihood estimator is emphasized by simulations.