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Moving Target Tracking in Three Dimensional Space with Wireless Sensor Network

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Abstract

In three-dimensional space, current target tracking algorithms based on wireless sensor networks are mainly non-iterative and operated with only current measurement result. A typical example is the least square algorithm. Compared with iterative algorithms which use historical information, such as extended Kalman filter, non-iterative algorithms always achieve lower accuracy but can avoid the dependence upon prior knowledge of system noises. In this letter, we firstly proposed a minimum residual localization algorithm based on particle swarm optimization, which is a non-iterative algorithm. Then, a data-fitting strategy is adopted to convert the non-iterative algorithm into iterative one without knowledge of system noise. Hence, the historical information can be used to improve the accuracy of non-iterative algorithm significantly. Simulation results show that the proposed algorithm acquires better localization result with strong adaptability for different motion.

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Acknowledgments

This letter is partly supported by National Natural Science Foundation of China (No. 61471110, 61273078), China Postdoctoral Science Foundation (No. 2014T 70263, 2012M511164) and the Scientific Research Fundation of Liaoning Provincial Education Department (No. L20150185).

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Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China

    Ning Xu, Yunzhou Zhang, Shuying Zhao & Wenyan Fu

  2. Faculty of Information Technology, Macau University of Science and Technology, Macau, China

    Du Zhang

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  1. Ning Xu
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  2. Yunzhou Zhang
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  3. Du Zhang
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  4. Shuying Zhao
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  5. Wenyan Fu
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Correspondence to Yunzhou Zhang.

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Xu, N., Zhang, Y., Zhang, D. et al. Moving Target Tracking in Three Dimensional Space with Wireless Sensor Network. Wireless Pers Commun 94, 3403–3413 (2017). https://doi.org/10.1007/s11277-016-3783-x

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  • DOI: https://doi.org/10.1007/s11277-016-3783-x

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