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Sensors 2015, 15(11), 29661-29684; doi:10.3390/s151129661

The Indoor Localization and Tracking Estimation Method of Mobile Targets in Three-Dimensional Wireless Sensor Networks

1
College of Information Science and Engineering, Northeastern University, NO. 3-11 Wenhua Road Heping District, Shenyang 110819, China
2
Anshan Industrial Technology Research Institute, Harbin Institute of Technology, 192 Central Qianshan Road, Anshan High-Tech Zone, Anshan 114000, China
3
Department of Electrical Engineering and Computer Science, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 1 September 2015 / Revised: 17 November 2015 / Accepted: 18 November 2015 / Published: 24 November 2015
(This article belongs to the Section Sensor Networks)
View Full-Text   |   Download PDF [515 KB, uploaded 24 November 2015]   |  

Abstract

Indoor localization is a significant research area in wireless sensor networks (WSNs). Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS). However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley–Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA) is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot’s motion model based on dead reckoning, which is the process of determining one’s current position, we employ the H ∞ filter and the strong tracking filter (STF) to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP) is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM) to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the H ∞ filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP. View Full-Text
Keywords: WSNs; three-dimensional deployment; calibration; localization WSNs; three-dimensional deployment; calibration; localization
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jia, Z.; Wu, C.; Li, Z.; Zhang, Y.; Guan, B. The Indoor Localization and Tracking Estimation Method of Mobile Targets in Three-Dimensional Wireless Sensor Networks. Sensors 2015, 15, 29661-29684.

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