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Open AccessArticle

Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation

1
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China
2
Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China
3
Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
4
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
5
Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Xue-Bo Jin
Sensors 2016, 16(5), 686; https://doi.org/10.3390/s16050686
Received: 17 March 2016 / Revised: 28 April 2016 / Accepted: 29 April 2016 / Published: 23 May 2016
(This article belongs to the Special Issue Advances in Multi-Sensor Information Fusion: Theory and Applications)
A compressive sensing joint sparse representation direction of arrival estimation (CSJSR-DoA) approach is proposed for wireless sensor array networks (WSAN). By exploiting the joint spatial and spectral correlations of acoustic sensor array data, the CSJSR-DoA approach provides reliable DoA estimation using randomly-sampled acoustic sensor data. Since random sampling is performed at remote sensor arrays, less data need to be transmitted over lossy wireless channels to the fusion center (FC), and the expensive source coding operation at sensor nodes can be avoided. To investigate the spatial sparsity, an upper bound of the coherence of incoming sensor signals is derived assuming a linear sensor array configuration. This bound provides a theoretical constraint on the angular separation of acoustic sources to ensure the spatial sparsity of the received acoustic sensor array signals. The Cram e ´ r–Rao bound of the CSJSR-DoA estimator that quantifies the theoretical DoA estimation performance is also derived. The potential performance of the CSJSR-DoA approach is validated using both simulations and field experiments on a prototype WSAN platform. Compared to existing compressive sensing-based DoA estimation methods, the CSJSR-DoA approach shows significant performance improvement. View Full-Text
Keywords: wireless sensor network; sensor array; compressive sensing; array processing wireless sensor network; sensor array; compressive sensing; array processing
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MDPI and ACS Style

Yu, K.; Yin, M.; Luo, J.-A.; Wang, Y.; Bao, M.; Hu, Y.-H.; Wang, Z. Wireless Sensor Array Network DoA Estimation from Compressed Array Data via Joint Sparse Representation. Sensors 2016, 16, 686.

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