Mobile Target Localization Algorithm Using Compressive Sensing in Wireless Sensor Networks

SUN Baoming; GUO Yan; LI Ning; QIAN Peng

doi:10.11999/JEIT151203

Volume 38 Issue 8

Sep. 2016

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SUN Baoming, GUO Yan, LI Ning, QIAN Peng. Mobile Target Localization Algorithm Using Compressive Sensing in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1858-1864. doi: 10.11999/JEIT151203

Citation:

SUN Baoming, GUO Yan, LI Ning, QIAN Peng. Mobile Target Localization Algorithm Using Compressive Sensing in Wireless Sensor Networks[J]. Journal of Electronics & Information Technology, 2016, 38(8): 1858-1864. doi: 10.11999/JEIT151203

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Mobile Target Localization Algorithm Using Compressive Sensing in Wireless Sensor Networks

doi: 10.11999/JEIT151203 cstr: 32379.14.JEIT151203

Funds:

The National Natural Science Foundation of China (61571463, 61371124, 61272487, 61472445, 61201217)

Received Date: 2015-10-29
Rev Recd Date: 2016-03-29
Publish Date: 2016-08-19

Abstract

Abstract

Traditional mobile target localization algorithms are not suitable for wireless sensor networks as they need to collect, store, and process a mass of data. To address this issue, a mobile target localization algorithm based on compressive sensing is proposed. Two sparse representation bases are designed by exploiting the movement characteristics of mobile targets, therefore the mobile target localization issue is transferred into a sparse signal recovery issue. To avoid the unpractical limitation of traditional measurement matrices, two sparse measurement matrices are proposed that are practical and lowly coherent with the designed representation bases. The characteristic of this algorithm is that mobile target localization can be achieved by collecting a little data, thus prolonging the lifetime of wireless sensor networks. Simulation results indicate that the proposed localization algorithm based on compressive sensing is highly efficient.
- Wireless sensor networks,
- Mobile target localization,
- Compressive sensing,
- Sparse representation basis,
- Measurement matrix

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References(23)

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