A novel ML DOA estimation algorithm for array signal processing

A novel algorithm for finding the optimal solution of nonlinear function in maximum likelihood DOA estimation is proposed to reduce the calculation in multi-dimensional nonlinear search of the estimation. In the proposed method, firstly, the mode of population initialization mode is modified to impr...

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Published inMATEC web of conferences Vol. 232; p. 3055
Main Authors Shi, Pengfei, Fan, Xinnan, Ni, Jianjun, Yang, Dewei
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2018
Subjects
Algorithms
Crossovers
Mathematical analysis
Maximum likelihood estimation
Signal processing
Online AccessGet full text
ISSN2261-236X
2274-7214
2261-236X
DOI10.1051/matecconf/201823203055

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Abstract A novel algorithm for finding the optimal solution of nonlinear function in maximum likelihood DOA estimation is proposed to reduce the calculation in multi-dimensional nonlinear search of the estimation. In the proposed method, firstly, the mode of population initialization mode is modified to improve the stability of population evolution. Secondly, the crossover operator is also improved to enlarge the range of new generated individual. Thirdly, parameters adaptive adjustment strategy is designed to accelerate convergence. The simulation shows that the proposed algorithm can greatly reduce the calculation time.
AbstractList A novel algorithm for finding the optimal solution of nonlinear function in maximum likelihood DOA estimation is proposed to reduce the calculation in multi-dimensional nonlinear search of the estimation. In the proposed method, firstly, the mode of population initialization mode is modified to improve the stability of population evolution. Secondly, the crossover operator is also improved to enlarge the range of new generated individual. Thirdly, parameters adaptive adjustment strategy is designed to accelerate convergence. The simulation shows that the proposed algorithm can greatly reduce the calculation time.
Author Fan, Xinnan
Ni, Jianjun
Yang, Dewei
Shi, Pengfei
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10.1109/CEC.2014.6900618
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