Reiterative robust adaptive thresholding for nonhomogeneity detection in non-gaussian noise
EURASIP Journal on Advances in Signal Processing
CFAR detection strategies for distributed targets under conic constraints
IEEE Transactions on Signal Processing
Knowledge-aided covariance matrix estimation and adaptive detection in compound-Gaussian noise
IEEE Transactions on Signal Processing
A covariance matrix based approach to internet anomaly detection
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Digital Signal Processing
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Adaptive detection of signals embedded in Gaussian or non-Gaussian noise is a problem of primary concern among radar engineers. We propose a recursive algorithm to estimate the structure of the covariance matrix of either a set of Gaussian vectors that share the spectral properties up to a multiplicative factor or a set of spherically invariant random vectors (SIRVs) with the same covariance matrix and possibly correlated texture components. We also assess the performance of an adaptive implementation of the normalized matched filter (NMF), relying on the newly introduced estimate, in the presence of compound-Gaussian, clutter-dominated, disturbance. In particular, it is shown that a proper initialization of the recursive procedure leads to an adaptive NMF with the constant false alarm rate (CFAR) property and that it is very effective to operate in heterogeneous environments of relevant practical interest