Advances in Spectrum Analysis and Array Processing, Volume 3
Prentice Hall, Jan 1, 1995 - Signal processing. - 542 pages
In this, the third and final volume in the series, ten experts investigate a broad range of topics covering fundamental issues and applications in popular and new algorithms for Spectral Analysis and Array Processing. It covers optimal model-based processing techniques for the detection of multiple narrowband sources; two-dimensional angle estimation; direction-finding algorithms for closely-spaced source scenarios; and the use of neural networks in solving source location problems.
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Fundamental Limitations on Direction Finding
Robustness and Sensitivity Analysis for Eigenspace
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actual sensors algorithm amplitude analysis angle estimation array processing ASSP assumed asymptotic azimuth beamformer columns components compute corresponding covariance matrix CR bound criterion cross-correlation cumulant-based defined denotes derived detection direction DOA estimation eigenvalues eigenvectors element space MUSIC equations example expressions far-field Figure Fisher information fourth-order cumulants function Gaussian given guiding sensors identical IEEE IEEE Trans likelihood function locations maximize Maximum Likelihood measurements methods minimization ML estimator multiple MUSIC algorithm MVDR narrowband Nehorai noise subspace non-Gaussian non-Gaussian noise null number of signals observation obtained optimal optimum weighting orthogonal parameters performance perturbation Phase-locked Loops problem resolution sample covariance matrix samples sensor array Signal Processing signal subspace simulation space MUSIC spatial spectral spectrum statistics steering vector subarray Taylor series Theorem UCA-ESPRIT UCA-RB-MUSIC UN-CLE algorithm UN-MUSIC uniform linear array unknown virtual sensors virtual-ESPRIT weight vector white noise