General Improvements of Heuristic Algorithms for Low Complexity DOA Estimation

Heuristic algorithms are considered to be effective approaches for super-resolution DOA estimations such as Deterministic Maximum Likelihood (DML), Stochastic Maximum Likelihood (SML), and Weighted Subspace Fitting (WSF) which are involved in nonlinear multi-dimensional optimization. Traditional heu...

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Bibliographic Details
Published in:International Journal of Antennas and Propagation
Main Authors: Haihua Chen, Haoran Li, Mingyang Yang, Changbo Xiang, Masakiyo Suzuki
Format: Article in Journal/Newspaper
Language:English
Published: Wiley 2019
Subjects:
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Cellular telephone services industry. Wireless telephone industry
HE9713-9715
DML
Online Access:https://doi.org/10.1155/2019/3858794
https://doaj.org/article/2a1810cf006e4770b5842a6ba6427e7d
Description
Summary:Heuristic algorithms are considered to be effective approaches for super-resolution DOA estimations such as Deterministic Maximum Likelihood (DML), Stochastic Maximum Likelihood (SML), and Weighted Subspace Fitting (WSF) which are involved in nonlinear multi-dimensional optimization. Traditional heuristic algorithms usually need a large number of particles and iteration times. As a result, the computational complexity is still a bit high, which prevents the application of these super-resolution techniques in real systems. To reduce the computational complexity of heuristic algorithms for these super-resolution techniques of DOA, this paper proposes three general improvements of heuristic algorithms, i.e., the optimization of the initialization space, the optimization of evolutionary strategies, and the usage of parallel computing techniques. Simulation results show that the computational complexity can be greatly reduced while these improvements are used.

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