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A Refined Genetic Algorithm for Accurate and Reliable DOA Estimation with a Sensor Array

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Abstract

Maximum likelihood (ML) direction-of-arrival (DOA) estimation algorithm is a nearly optimal technique. In this paper, we present a modified and refined genetic algorithm (GA) to find the exact solutions to the complex, multi-modal, multivariate and highly nonlinear likelihood function. With the newly introduced features such as intelligent initialization and the emperor-selective mating scheme, carefully selected crossover and mutation operators, and fine-tuned parameters such as the population size, the probability of crossover and mutation, the GA-ML estimator achieves fast global convergence. The GA-ML estimator has been compared with various DOA estimation methods in a variety of scenarios, and the simulation results demonstrate that in most scenarios the proposed GA-ML estimator is the fastest and its performance is the best among popular ML-based DOA estimation methods.

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Authors and Affiliations

  1. Intelligent Systems Center, Nanyang Technological University, Research Techno Plaza 7th Storey, BorderX Block 50, Nanyang Drive, Singapore, 637553, Singapore

    Minghui Li

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Yilong Lu

Authors
  1. Minghui Li
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  2. Yilong Lu
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Correspondence to Minghui Li.

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Li, M., Lu, Y. A Refined Genetic Algorithm for Accurate and Reliable DOA Estimation with a Sensor Array. Wireless Pers Commun 43, 533–547 (2007). https://doi.org/10.1007/s11277-007-9248-5

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  • DOI: https://doi.org/10.1007/s11277-007-9248-5

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