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Improved Wi-Fi Indoor Positioning Based on Particle Swarm Optimization

机译：基于粒子群算法的改进型Wi-Fi室内定位

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摘要

Indoor positioning methods based on the received signal strength indication (RSSI) ranging technology are sensitive to various environmental noises, which cause positioning errors. An improved Wi-Fi indoor positioning method using an improved unscented Kalman filter and the particle swarm optimization (PSO) is proposed to reduce ranging error and improve positioning accuracy. The received signals are preprocessed by the improved unscented Kalman filter algorithm and then the improved PSO algorithm is used to optimize position calculation results. To demonstrate the utility of the proposed algorithm, simulations and experiments were performed for estimating the position of objects. Simulating results indicate that the mean error of the proposed algorithm is reduced by 31.87% in comparison with that of the unlicensed Kalman filter method. Experimental results show that the mean error of the proposed algorithm is reduced by 26.72% in comparison with that of the unlicensed Kalman filter method. Therefore, the proposed algorithm can effectively reduce the positioning error and improve the positioning accuracy. In the actual indoor positioning, it could get better positioning results.
机译：基于接收信号强度指示（RSSI）测距技术的室内定位方法对各种环境噪声敏感，这些噪声会导致定位错误。为了减少测距误差并提高定位精度，提出了一种改进的Wi-Fi室内定位方法，该方法使用改进的无味卡尔曼滤波器和粒子群算法（PSO）。通过改进的无味卡尔曼滤波算法对接收到的信号进行预处理，然后使用改进的PSO算法优化位置计算结果。为了证明所提出算法的实用性，进行了仿真和实验以估计物体的位置。仿真结果表明，与非授权卡尔曼滤波方法相比，该算法的平均误差降低了31.87％。实验结果表明，与无执照卡尔曼滤波方法相比，该算法的平均误差降低了26.72％。因此，提出的算法可以有效减少定位误差，提高定位精度。在实际的室内定位中，可以获得更好的定位效果。

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  • 来源
    《IEEE sensors journal》 |2017年第21期|7143-7148|共6页
  • 作者

    Xiao Chen; Shengnan Zou;

  • 作者单位

    Jiangsu Key Laboratory of Meteorological Observation and Information Processing and the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China;

    Jiangsu Key Laboratory of Meteorological Observation and Information Processing and the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Kalman filters; Estimation; Distance measurement; Robustness; Covariance matrices; Mathematical model;

    机译：卡尔曼滤波;估计;距离测量;稳健性;协方差矩阵;数学模型;

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