advanced
Jia-Zhou Liu, Zhi-Qin Zhao, Zi-Yuan He, Qing-Huo Liu. DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization[J]. Journal of Electronic Science and Technology, 2014, 12(1): 71-75. DOI: 10.3969/j.issn.1674-862X.2014.01.014
Citation: Jia-Zhou Liu, Zhi-Qin Zhao, Zi-Yuan He, Qing-Huo Liu. DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization[J]. Journal of Electronic Science and Technology, 2014, 12(1): 71-75. DOI: 10.3969/j.issn.1674-862X.2014.01.014
shu

DOA and Power Estimation Using Genetic Algorithm and Fuzzy Discrete Particle Swarm Optimization

doi: 10.3969/j.issn.1674-862X.2014.01.014
  • 1.

    School of Electronic Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;

  • 2.

    Department of Electrical and Computer Engineering,Duke University,Durham NC 27708,USA

More Information
  • Author Bio:

    Jia-Zhou Liu research interests include array signal processing and conformal antenna array, jiazhou.liu@gmail.com;
    Zhi-Qin Zhao, zqzhao@uestc.edu.cn;
    Qing-Huo Liu, qhliu@duke.edu

    Jia-Zhou Liu research interests include array signal processing and conformal antenna array, jiazhou.liu@gmail.com;
    Zhi-Qin Zhao, zqzhao@uestc.edu.cn;
    Qing-Huo Liu, qhliu@duke.edu

    Jia-Zhou Liu research interests include array signal processing and conformal antenna array, jiazhou.liu@gmail.com;
    Zhi-Qin Zhao, zqzhao@uestc.edu.cn;
    Qing-Huo Liu, qhliu@duke.edu

  • Authors’ information: Jia-Zhou Liu
  • Received Date: 2013-05-13
  • Rev Recd Date: 2013-08-20
  • Publish Date: 2014-03-24
    Abstract
  • Aiming to reduce the computational costs and converge to global optimum, a novel method is proposed to solve the optimization of a cost function in the estimation of direction of arrival (DOA). In this method, genetic algorithm (GA) and fuzzy discrete particle swarm optimization (FDPSO) are applied to optimize the direction of arrival and power parameters of the mode simultaneously. Firstly, the GA algorithm is applied to make the solution fall into the global searching. Secondly, the FDPSO method is utilized to narrow down the search field. In FDPSO, chaotic factor and crossover method are added to speed up the convergence. This approach has been demonstrated through some computational simulations. It is shown that the proposed algorithm can estimate both the DOA and the powers accurately. It is more efficient than some present methods, such as Newton-like algorithm, Akaike information critical (AIC), particle swarm optimization (PSO), and genetic algorithm with particle swarm optimization (GA-PSO).
    • FullText(HTML)
    • References(0)
    • Relative Articles

  • Related Articles

    [1]Chien-Hao Su, Chien-Shun Chiou, Jung-Che Kuo, Pei-Jen Wang, Cheng-Yan Kao, Hsueh-Ting Chu. Family Competition Pheromone Genetic Algorithm for Comparative Genome Assembly[J]. Journal of Electronic Science and Technology, 2014, 12(4): 405-409. DOI: 10.3969/j.issn.1674-862X.2014.04.012
    [2]Yu-Cheng Lin. A Genetic Algorithm Based Approach for Campus Equipment Management System in Cloud Server[J]. Journal of Electronic Science and Technology, 2013, 11(2): 187-191. DOI: 10.3969/j.issn.1674-862X.2013.02.011
    [3]Ping-Liang Chen, Yu-Cheng Lin, Shin-Jia Chen. Solving Dynamic Spectrum Management Problem Based on Cloud Computing Using Genetic Algorithm[J]. Journal of Electronic Science and Technology, 2013, 11(2): 132-139. DOI: 10.3969/j.issn.1674-862X.2013.02.004
    [4]Xin-Yu Du, Yong-Jie Li, Cheng Luo, De-Zhong Yao. Elitist Reconstruction Genetic Algorithm Based on Markov Random Field for Magnetic Resonance Image Segmentation[J]. Journal of Electronic Science and Technology, 2012, 10(1): 83-87. DOI: 10.3969/j.issn.1674-862X.2012.01.015
    [5]Hao-Dong Zhu, Hong-Chan Li, Xiang-Hui Zhao, Yong Zhong. Feature Selection Method by Applying Parallel Collaborative Evolutionary Genetic Algorithm[J]. Journal of Electronic Science and Technology, 2010, 8(2): 108-113. DOI: 10.3969/j.issn.1674-862X.2010.02.003
    [6]Xiao-Ling Zhang, Li Du, Guang-Wei Zhang, Qiang Miao, Zhong-Lai Wang. An Improved Genetic Algorithm with Quasi-Gradient Crossover[J]. Journal of Electronic Science and Technology, 2008, 6(1): 47-51.
    [7]Zhong-Lai Wang, Ping Yang, Dan Ling, Qiang Miao. An Improved Real-Coded Genetic Algorithm and Its Application[J]. Journal of Electronic Science and Technology, 2008, 6(1): 43-46.
    [8]Da-Qing Guo, Yong-Jin Zhao, Hui Xiong, Xiao Li. A New Class of Hybrid Particle Swarm Optimization Algorithm[J]. Journal of Electronic Science and Technology, 2007, 5(2): 149-152.
    [9]ZUO Guo-yu, GONG Dao-xiong, RUAN Xiao-gang. A Linkage Learning Genetic Algorithm with Linkage Matrix[J]. Journal of Electronic Science and Technology, 2006, 4(1): 29-34.
    [10]ZHU Lili, ZHANG Huanchun, JING Yazhi. A New Neuro-Fuzzy Adaptive Genetic Algorithm[J]. Journal of Electronic Science and Technology, 2003, 1(1): 63-68.
    • Supplements (0)

    • Catalog

      Get Citation
      PDF
      XML

      Article Metrics

      Article views (343) PDF downloads (27) Cited by()
      Related
      Proportional views

      Copyright 2008-2016 JEST All Rights Reserved

      Supported by: Beijing Renhe Information Technology Co. Ltd 

      /

      DownLoad:  Full-Size Img  PowerPoint
      Return
      Return