The expectation maximization (em) algorithm is presented for the case of estimating direction of arrivals of unknown deterministic wideband signals, Alternative regularized least squares estimation techniques for the ...
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The expectation maximization (em) algorithm is presented for the case of estimating direction of arrivals of unknown deterministic wideband signals, Alternative regularized least squares estimation techniques for the required signal estimation and a tree structure for the data mapping in the Ehl algorithm are proposed. Extensive simulation results are presented for comparison of the proposed algorithms with the conventional em approach and the current high-resolution methods of wideband direction finding.
As an extension to the conventional emalgorithm, tree-structuredem algo rithm is proposed for the ML estimation of parameters of superimposed signals. For the special case of superimposed signals in Gaussian n...
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As an extension to the conventional emalgorithm, tree-structuredem algo rithm is proposed for the ML estimation of parameters of superimposed signals. For the special case of superimposed signals in Gaussian noise, the IQML al gorithm of Bresler and Macovski [19] is incorporated to the M-step of the em based algorithms resulting in more efficient and reliable maximization. Based on simulations, it is observed that TSem converges significantly faster than em, but it is more sensitive to the initial parameter estimates. Hybrid-em al gorithm, which performs a few em iterations prior to the TSem iterations, is proposed to capture the desired features of both the em and TSemalgorithms. Based on simulations, it is found that Hybrid-emalgorithm has significantly more robust convergence than both the em and TSemalgorithms.
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