Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information

Dongping YU; Yan GUO; Ning LI; Jie LIU; Sixing YANG

doi:10.11999/JEIT180333

Volume 41 Issue 2

Jan. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(2): 440-446

Dongping YU, Yan GUO, Ning LI, Jie LIU, Sixing YANG. Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information[J]. Journal of Electronics & Information Technology, 2019, 41(2): 440-446. doi: 10.11999/JEIT180333

Citation:

Dongping YU, Yan GUO, Ning LI, Jie LIU, Sixing YANG. Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information[J]. Journal of Electronics & Information Technology, 2019, 41(2): 440-446. doi: 10.11999/JEIT180333

Citation:

Dongping YU, Yan GUO, Ning LI, Jie LIU, Sixing YANG. Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information[J]. Journal of Electronics & Information Technology, 2019, 41(2): 440-446. doi: 10.11999/JEIT180333

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Compressive Sensing Based Multi-target Device-free Passive Localization Algorithm Using Multidimensional Measurement Information

doi: 10.11999/JEIT180333

1.
College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China
2.
The Chinese Armed Police Force, Beijing 100089, China

Funds: The National Natural Science Foundation of China (61871400, 61571463), The Natural Science Foundation of Jiangsu Province (BK20171401)

Received Date: 2018-04-11
Rev Recd Date: 2018-11-01

Available Online: 2018-11-09

Publish Date: 2019-02-01

Abstract

Abstract

Device-free passive localization is a key issue of the intruder detection, environmental monitoring, and intelligent transportation. The existing device-free passive localization method can obtain the multidimensional measurement information by channel state information, but the existing scheme can not fully exploit the frequency diversity on multiple channels to improve the localization performance. This paper proposes a Compressive Sensing (CS) based multi-target device-free passive localization algorithm using multidimensional measurement information. It takes advantage of the frequency diversity of multidimensional measurement information to improve the accuracy and robustness of localization results under the CS framework. The dictionary is built according to the saddle surface model, and the multi-target device-free passive localization problem is modeled as a joint sparse recovery problem based on multiple measurement vectors. The target location vector is estimated based on the multiple sparse Bayesian learning algorithm. Simulation results indicate that the proposed algorithm can make full use of the multidimensional measurement information to improve the localization performance.
- Device-free passive localization,
- Compressive Sensing (CS),
- Multiple measurement vectors,
- Sparse Bayesian Learning (SBL)

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References(21)

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