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Volume 41 Issue 1
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(1): 65-70.
JIAO Yameng, HUANG Jianguo, HAN Jinget al. Fast Bayesian DOA estimator using continuous ant colony optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 65-70. doi: 10.13700/j.bh.1001-5965.2014.0051(in Chinese)
Citation: JIAO Yameng, HUANG Jianguo, HAN Jinget al. Fast Bayesian DOA estimator using continuous ant colony optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(1): 65-70. doi: 10.13700/j.bh.1001-5965.2014.0051(in Chinese)
shu

Fast Bayesian DOA estimator using continuous ant colony optimization

doi: 10.13700/j.bh.1001-5965.2014.0051
  • 1.

    College of Electronics and Information, Xi'an Polytechnic University, Xi'an 710048, China

  • 2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China

  • Received Date: 27 Jan 2014
  • Publish Date: 20 Jan 2015
    Abstract
  • For the effectiveness of the ant colony optimization algorithm for solving high-dimensional nonlinear search problem, a Bayesian maximum posteriori direction of arrival (DOA) estimation fast algorithm based on the ant colony optimization algorithm (ACO-Bayesian) was proposed. This algorithm adopts Bayesian maximum posteriori probability function as the objective function of the ant colony algorithm, exploits a weighted sum of several one-dimensional Gaussian functions in the sampling process. The global maximum of Bayesian spatial spectrum function can be reached after reasonable iterations. Simulation results show that the proposed algorithm provides similar performance to that achieved by Bayesian estimator, but its computational complexity cost is only 1/14 of original method. The water tank experiment results verified the correctness and validity of the proposed ACO-Bayesian method, which promote them to promising in engineering applications.

     

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