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Expected Likelihood based quality assessment of DOA estimates relies on the underlying signal and noise distributions having likelihood ratio probability density functions for the (unknown) true parameters that are independent of the actual true DOAs. This has been shown, both analytically and practically for a wide range of real and complex Gaussian solutions. Recent studies [1, 2] focusing on compound...
We reexamine the well-known problem of “threshold behavior” or “performance breakdown” in the detection-estimation of very closely spaced emitters. In this extreme regime, we analyze the performance for maximum-likelihood estimation (MLE) of directions-of-arrival (DOA) for two close Gaussian sources over the range of sample volumes and signal-to-noise ratios (SNRs) where the correct number of sources...
Even for the simple rank-one plane-wave model, the accurate maximum-likelihood (ML) solution of the detection-estimation problem is infeasible, mainly because the multivariate likelihood function is non-convex and multi-extremal. For this reason, a number of "ML-proxy" routines have been developed that, in some important practical cases, approach the efficiency of the ML performance benchmark...
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