Adaptive Grid Multiple Sources Localization Based on Sparse Bayesian Learning

Kangyong YOU; Lishan YANG; Yueliang LIU; Wenbin GUO; Wenbo WANG

doi:10.11999/JEIT171238

Volume 40 Issue 9

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(9): 2150-2157

Kangyong YOU, Lishan YANG, Yueliang LIU, Wenbin GUO, Wenbo WANG. Adaptive Grid Multiple Sources Localization Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2150-2157. doi: 10.11999/JEIT171238

Citation:

Kangyong YOU, Lishan YANG, Yueliang LIU, Wenbin GUO, Wenbo WANG. Adaptive Grid Multiple Sources Localization Based on Sparse Bayesian Learning[J]. Journal of Electronics & Information Technology, 2018, 40(9): 2150-2157. doi: 10.11999/JEIT171238

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Adaptive Grid Multiple Sources Localization Based on Sparse Bayesian Learning

doi: 10.11999/JEIT171238

Kangyong YOU^{1, 2},
Lishan YANG^{1, 2},
Yueliang LIU¹,
Wenbin GUO^{1, 2
,
,},
Wenbo WANG¹

1.
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
2.
Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory, Shijiazhuang 050000, China

Funds: The National Natural Science Foundation of China (61271181, 61571054), The Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Foundation

Received Date: 2017-12-28
Rev Recd Date: 2018-05-23

Available Online: 2018-07-12

Publish Date: 2018-09-01

Abstract

Abstract

Multiple sources localization is an issue of theoretical importance and practical significance in signal processing. The basis mismatch problem caused by target deviation from the initial grid point is addressed. Based on sparse Bayesian learning framework with Laplace prior, a novel iterative Adaptive Grid Multiple Targets Localization (AGMTL) algorithm is proposed to tackle the practical situation in which the targets deviates from the initial grid point. In essence, AGMTL algorithm implements sparse signal reconstruction and adaptive grid localization dictionary learning jointly. The simulation results show that AGMTL algorithm outperforms the traditional Compressive Sensing (CS) based localization algorithm in the terms of localization error, estimation reliability and noise robustness.
- Multiple sources localization,
- Compressive Sensing (CS),
- Adaptive grid model,
- Sparse Bayesian learning,
- Laplace prior

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References(16)

References

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