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A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network

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Abstract

Ranging error is known to degrade significantly the target node localization accuracy. This paper investigates the use of computationally efficient positioning solution of least square (LS) in closed-form, to reduce localization accuracy loss caused by ranging error. For range-based node localization, the LS solution based on least square criterion has been confirmed to exhibit capability of optimum estimation but extensively achieve at a very complex calculation. In this paper we consider the problem how to acquire such LS solution provided with estimation performance at low complex calculation. In this paper, we use the Gauss noise model and use the weighted least squares criterion and the effective calculation method to solve the linearized equation derived from the RSS measurement, and put forward a new approach to estimate the performance of the target node location estimation. Based on the Fisher information matrix, the Cramér–Rao lower bound of target position estimation is derived based on received signal strength. We obviously indicate that the proposed algorithm can approximately achieve the LS solution in estimation performance at a markedly low complex calculation. Simulations are performed to show the improvement of the proposed algorithm.

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Acknowledgements

The authors would like to thank all of the discussion and suggestions for the help of this paper. This research was supported by The National Natural Science Foundation of China (Grant No. 21276111).

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Authors and Affiliations

  1. School of Internet of Things Engineering, JiangNan University, Wuxi, 214122, China

    Xinrong Zhang, Weili Xiong & Baoguo Xu

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  1. Xinrong Zhang
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  2. Weili Xiong
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  3. Baoguo Xu
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Correspondence to Xinrong Zhang.

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Zhang, X., Xiong, W. & Xu, B. A Computationally Efficient Received Signal Strength Based Localization Algorithm in Closed-Form for Wireless Sensor Network. Neural Process Lett 46, 1043–1057 (2017). https://doi.org/10.1007/s11063-017-9625-3

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  • DOI: https://doi.org/10.1007/s11063-017-9625-3

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