Abstract
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.
Highlights
The monitoring of noncooperative targets using spatially distributed arrays has been one of the key problems in many applications such as biological acoustic studies [1], gunshot localization [2], and military target tracking [3]
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
Based on compressive sensing (CS) [8, 9], we propose a compressed sampling and collaborative reconstruction framework that can achieve obvious data reduction while keeping similar direction of arrival (DoA) performance
Summary
The monitoring of noncooperative targets using spatially distributed arrays has been one of the key problems in many applications such as biological acoustic studies [1], gunshot localization [2], and military target tracking [3]. Sensors belonging to the same array often receive signal with the same frequency components, with only a phase shift due to time difference of arrival, because they are different in distance to the source Leveraging these two sparsities with compressive sensing theory, we could reduce the data volume to our best. After successful reconstruction of the array data at the fusion center, either conventional DoA methods such as MUSIC [16] or the sparse representation based approach [12] may be applicable. (c) Leveraging the high correlation of acoustic signals among array elements, a collaborative reconstruction method, works for both random compressed sampling and 1-bit compressive sensing and is presented to drastically reduce computation cost and delay.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
