The maximum likelihood (ML) angle estimator can yield optimal angle estimation performance. In the work presented here, a fast algorithm for solving the global optimal solution of the ML angle estimator based on principal component analysis (PCA) and grid search (GS) is developed. Utilizing the low-rank property of the mainbeam steering matrix, the log-likelihood function can be decomposed as a combination of the relevant quantities of basis vectors of the low-rank subspace. Thus, evaluation of the log-likelihood function can be realized in a lower dimensional space. Although GS is also required, the computational complexity can be greatly reduced, and the global optimal solution can be obtained.
Angle Estimation for Adaptive Linear Array using PCA-GS-ML Estimator
IEEE Transactions on Aerospace and Electronic Systems ; 49 , 1 ; 670-677
2013-01-01
2006562 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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