Indoor precise positioning algorithm using 60GHz pulse based on compressive sensing

Volume 16, Issue 2, pp 256-272 http://dx.doi.org/10.22436/jmcs.016.02.14

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Authors

Xueyan Han - School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong Province, China. Jingjing Wang - School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong Province, China. Wei Shi - School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong Province, China. Qiuna Niu - School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong Province, China. Lingwei Xu - School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, Shandong Province, China.

Abstract

Aiming at the existing defect of poor positioning accuracy in NLOS (Non Line of Sight) environment for most of the common indoor positioning algorithms, this paper proposes a precise indoor positioning algorithm using 60GHz pulse based on compressed sensing. The proposed algorithm converts the location of the target nodes in the area to be located into a sparse vector and designs the over-completed dictionary using TOA (Time of Arrival)-based ranging, then takes advantage of the \(l_1\)-minimization to reconstruct the location of the target nodes. The algorithm divides the positioning process into coarse positioning and fine positioning, and introduces the reference node selection mechanism in fine positioning. The algorithm not only can achieve the positioning of single target, but also achieve the positioning of multiple targets. Through the theoretical analysis and experiment simulation results, we can conclude that the proposed algorithm using 60GHz pulse can achieve precise indoor positioning in NLOS environment and centimeter-level positioning precision can be obtained compared with TOA based 60GHz geometric positioning algorithm.

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Keywords

• Full conditional posterior density
• NLOS
• compressed sensing
• positioning.

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