Performance of ML Range Estimator in Radio Interferometric Positioning Systems

Yue Zhang; Wangdong Qi; Guangxia Li; Su Zhang

doi:10.1109/LSP.2014.2352270

Performance of ML Range Estimator in Radio Interferometric Positioning Systems

Information Theory 2015-06-19 v1 Networking and Internet Architecture math.IT

Authors: Yue Zhang , Wangdong Qi , Guangxia Li , Su Zhang

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Abstract

The radio interferometric positioning system (RIPS) is a novel positioning solution used in wireless sensor networks. This letter explores the ranging accuracy of RIPS in two configurations. In the linear step-frequency (LSF) configuration, we derive the mean square error (MSE) of the maximum likelihood (ML) estimator. In the random step-frequency (RSF) configuration, we introduce average MSE to characterize the performance of the ML estimator. The simulation results fit well with theoretical analysis. It is revealed that RSF is superior to LSF in that the former is more robust in a jamming environment with similar ranging accuracy.

Keywords

radio interferometric positioning system maximum likelihood estimator ranging accuracy linear step-frequency random step-frequency wireless sensor networks jamming environment

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@article{arxiv.1403.1323,
  title  = {Performance of ML Range Estimator in Radio Interferometric Positioning Systems},
  author = {Yue Zhang and Wangdong Qi and Guangxia Li and Su Zhang},
  journal= {arXiv preprint arXiv:1403.1323},
  year   = {2015}
}

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10 pages, 1 figure

Performance of ML Range Estimator in Radio Interferometric Positioning Systems

Abstract

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