Abstract / truncated to 115 words (read the full abstract)

This thesis addresses a number of problems all related to parameter estimation in sensor array processing. The unifying theme is that some of these parameters are known before the measurements are acquired. We thus study how to improve the estimation of the unknown parameters by incorporating the knowledge of the known parameters; exploiting this knowledge successfully has the potential to dramatically improve the accuracy of the estimates. For covariance matrix estimation, we exploit that the true covariance matrix is Kronecker and Toeplitz structured. We then devise a method to ascertain that the estimates possess this structure. Additionally, we can show that our proposed estimator has better performance than the state-of-art when the number of samples ... toggle 13 keywords

array signal processing – covariance matrix – damped sinusoids – direction of arrival estimation – frequency estimation – kronecker – NQR – NMR – parameter estimation – persymmetric – signal processing algorithms – structured covariance estimation – toeplitz

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