Workshop PFT

Research Article

A Modified Particle Filter Algorithm for Wireless Capsule Endoscope Location Tracking

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  • @INPROCEEDINGS{10.4108/icst.bodynets.2013.253650,
        author={Takahiro Ito and Daisuke Anzai and Jianqing Wang},
        title={A Modified Particle Filter Algorithm for Wireless Capsule Endoscope Location Tracking},
        proceedings={Workshop PFT},
        publisher={ICST},
        proceedings_a={PFT},
        year={2013},
        month={10},
        keywords={implant bans capsule endoscope location tracking particle filter},
        doi={10.4108/icst.bodynets.2013.253650}
    }
    
  • Takahiro Ito
    Daisuke Anzai
    Jianqing Wang
    Year: 2013
    A Modified Particle Filter Algorithm for Wireless Capsule Endoscope Location Tracking
    PFT
    ICST
    DOI: 10.4108/icst.bodynets.2013.253650
Takahiro Ito1, Daisuke Anzai1,*, Jianqing Wang1
  • 1: Nagoya Institute of Technology
*Contact email: anzai@nitech.ac.jp

Abstract

Tracking a capsule endoscope location is one of promising application offered by implant body area networks (BANs). In this paper, we pay attention to a particle filter algorithm with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem, which assumes a nonlinear transition model on the capsule endoscope location. However, the original particle filter requires to calculate the particle weight according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated by received wireless signal. Therefore, for the purpose of applying the particle filter to the capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrates that the proposed tracking methods with the modified particle filter can improve the performance as compared with not only the conventional maximum likelihood (ML) localization but also the original particle filter-based location tracking.