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Study of Blind Adaptive Beamforming Method for Multiple Sources Based on Genetic Algorithm

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Proceedings of the 2012 International Conference on Cybernetics and Informatics

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 163))

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Abstract

Blind adaptive beamforming method for multiple sources is studied. A new method based on genetic algorithm is presented. The algorithm can estimate weight vectors by defining a new cost function. At the same time, a global optimal solution of nonlinear weighting vector estimation is obtained by complex coding genetic algorithm. The coded parameters in the complex coding genetic algorithm are composed of real parts and imaginary parts of complex weight vectors. The fitness function equivalent to the objective function of the traditional optimization techniques is constructed by the new cost function. Computer simulation proves correctness of this method.

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References

  1. Wu Q, Wong KM (1996) Blind adaptive beamforming for cyclostationary signals. IEEE Trans Signal Proces 87:2768–2779

    Google Scholar 

  2. Dogan MC, Mendel JM (1994) Cumulant-based blind optimum beamforming. IEEE Trans Aero Electron Syst 30:722–741

    Article  Google Scholar 

  3. Ding Z, Nguyen T (2000) Stationary points of a kurtosis maximization algorithm for blind signal separation and antenna beamforming. IEEE Trans Signal Process 48:1587–1596

    Article  Google Scholar 

  4. Li HS, Zhao JW, Wang F (2009) Study of a blind beamforming method based on RBFNN. J Air Force Eng Univ 10:46–49

    Google Scholar 

  5. Chen JY, Wu Y (2011) Blind beamforming algorithm in strong jamming. Radar Sci Technol 9:150–154

    Google Scholar 

  6. Meng Y, Wang JK, Zhu J (2009) A linearly constrained LSCM algorithm based on subspace. J Electron Inform Technol 31:49–52

    Google Scholar 

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Acknowledgements

This work was financially supported by the Shandong Natural Science Foundation (ZR2011FQ039).

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Authors and Affiliations

  1. School of Information and Electronic Engineering Shandong Institute of Business and Technology, Yantai, 264005, China

    Yanwu Liu

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  1. Yanwu Liu
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Correspondence to Yanwu Liu .

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Editors and Affiliations

  1. College of Elementary Education, Chongqing Normal University, Chongqing, People's Republic of China

    Shaobo Zhong

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Liu, Y. (2014). Study of Blind Adaptive Beamforming Method for Multiple Sources Based on Genetic Algorithm. In: Zhong, S. (eds) Proceedings of the 2012 International Conference on Cybernetics and Informatics. Lecture Notes in Electrical Engineering, vol 163. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3872-4_262

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  • DOI: https://doi.org/10.1007/978-1-4614-3872-4_262

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-3871-7

  • Online ISBN: 978-1-4614-3872-4

  • eBook Packages: EngineeringEngineering (R0)

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