General Improvements of Heuristic Algorithms for Low Complexity DOA Estimation. (11th December 2019)

• Record Type: Journal Article
• Authors: Chen, Haihua; Li, Haoran; Yang, Mingyang; Xiang, Changbo; Suzuki, Masakiyo
• Other Names: Goudos Sotirios K. Academic Editor.
• Is Part Of: International journal of antennas and propagation. Volume 2019(2019)
• Publication Date: 2019-12-11
• Subjects: Electronic apparatus and appliances -- Periodicals; Antennas (Electronics) -- Periodicals; Radio wave propagation -- Periodicals; Antennes (Électronique); Ondes radioélectriques -- Propagation; Antennas (Electronics); Electronic apparatus and appliances; Radio wave propagation; Electronic journals; Periodicals; 621.382405
• DOI: 10.1155/2019/3858794
• Languages: English
• ISSNs: 1687-5869

Abstract

Abstract : 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.