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Array Shape Estimation Using Partitioned Eigenstructure Method with Sources in Unknown Localizations

by Changgeng Shuai, Shike Zhang, Jiaxuan Yang, Sitong Zhou

1 Institute of Noise & vibration, Naval University of Engineering, Wuhan, 430033, China
2 National Key Laboratory on Ship Vibration & Noise, Wuhan, 430033, China
The author can be reached at: mjianguo0722@163.com.

Sound & Vibration 2018, 52(4), 2-5. https://doi.org/10.32604/sv.2018.03724

Abstract

Advanced array processing approaches require accurate knowledge of the location of individual element in a sensor array. Most array shape estimation methods require the directions of sources. In this paper, an array shape estimation method based on eigen-decomposition is presented. The directions of sources do not need to be considered in advance and optimal array shape is generated through a series of iterations. To further improve the accuracy of this algorithm, a partitioned eigenstructure method is introduced. Numerical simulations using non-partitioned and partitioned method are conducted to verify the performance of the proposed technique.

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Cite This Article

APA Style
Shuai, C., Zhang, S., Yang, J., Zhou, S. (2018). Array shape estimation using partitioned eigenstructure method with sources in unknown localizations. Sound & Vibration, 52(4), 2-5. https://doi.org/10.32604/sv.2018.03724
Vancouver Style
Shuai C, Zhang S, Yang J, Zhou S. Array shape estimation using partitioned eigenstructure method with sources in unknown localizations. Sound Vib . 2018;52(4):2-5 https://doi.org/10.32604/sv.2018.03724
IEEE Style
C. Shuai, S. Zhang, J. Yang, and S. Zhou, “Array Shape Estimation Using Partitioned Eigenstructure Method with Sources in Unknown Localizations,” Sound Vib. , vol. 52, no. 4, pp. 2-5, 2018. https://doi.org/10.32604/sv.2018.03724



cc Copyright © 2018 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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