A Low-Complexity GA-WSF Algorithm for Narrow-Band DOA Estimation

This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. Howe...

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Bibliographic Details
Published inInternational journal of antennas and propagation Vol. 2018; no. 2018; pp. 1 - 6
Main Authors Liu, Jianhang, Li, Shibao, Tian, Hui, Hu, Jialiang, Chen, Haihua, Suzuki, Masakiyo
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Hindawi Limited
Wiley
Subjects
Artificial intelligence
Bands
Complexity
Computer simulation
Direction of arrival
Eigenvalues
Genetic algorithms
Hypotheses
Localization
Noise
Sensors
Signal processing
System effectiveness
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Summary:This paper proposes a low-complexity estimation algorithm for weighted subspace fitting (WSF) based on the Genetic Algorithm (GA) in the problem of narrow-band direction-of-arrival (DOA) finding. Among various solving techniques for DOA, WSF is one of the highest estimation accuracy algorithms. However, its criteria is a multimodal nonlinear multivariate optimization problem. As a result, the computational complexity of WSF is very high, which prevents its application to real systems. The Genetic Algorithm (GA) is considered as an effective algorithm for finding the global solution of WSF. However, conventional GA usually needs a big population size to cover the whole searching space and a large number of generations for convergence, which means that the computational complexity is still high. To reduce the computational complexity of WSF, this paper proposes an improved Genetic algorithm. Firstly a hypothesis technique is used for a rough DOA estimation for WSF. Then, a dynamic initialization space is formed around this value with an empirical function. Within this space, a smaller population size and smaller amount of generations are required. Consequently, the computational complexity is reduced. Simulation results show the efficiency of the proposed algorithm in comparison to many existing algorithms.
ISSN:1687-5869
1687-5877
DOI:10.1155/2018/7175653