Localization Bias Reduction in Wireless Sensor Networks

Yiming Ji, Changbin Yu, Junming Wei, Brian Anderson

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)

    Abstract

    In this paper, a novel bias-reduction method is proposed to analytically express and reduce the bias arising in sensor network localization problems, thereby improving the localization accuracy. The proposed bias-reduction method mixes Taylor series and a maximum-likelihood estimate and leads to an easily calculated analytical bias expression in terms of a known maximum-likelihood cost function. In contrast to existing contributions, this paper considers a sensor network as a whole when the bias is investigated by introducing the geometric structure of the sensor network into the proposed bias-reduction method via the rigidity matrix, a concept drawn from graph theory. The maximum-likelihood cost function is related to the rigidity matrix resulting in the final analytical expression of localization bias in terms of the rigidity matrix. Another important contribution of this paper is that, in addition to the presented simulation results, experimental results obtained by using a wireless localization system, which is developed based on reconfigurable software-defined radios (SDRs), are also provided to verify the performance of the proposed bias-reduction method. Both the simulation and experimental results demonstrate that the proposed method can reduce the bias, thereby improving the localization accuracy in different scenarios.

    Original languageEnglish
    Article number6920078
    Pages (from-to)3004-3016
    Number of pages13
    JournalIEEE Transactions on Industrial Electronics
    Volume62
    Issue number5
    DOIs
    Publication statusPublished - 1 May 2015

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