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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 9641608, 11 pages
Research Article

Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

College of Control Science and Engineering, Zhejiang University, Hangzhou, China

Received 31 March 2016; Accepted 1 August 2016

Academic Editor: Cornel Ioana

Copyright © 2016 Ming Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.