| 摘要: |
| 基于射频识别的指纹滤波定位技术是当前室内定位中常使用的技术之一。针对该技术存在的卡尔曼滤波算法不能准确适应环境噪声变化，致使定位精度不高的问题，提出了一种适应时变噪声的贝叶斯卡尔曼滤波算法。所提算法结合Sage-Husa滤波模型和贝叶斯模型，实现了过程和测量协方差矩阵的最优化，有效地降低了噪声，提高了指纹滤波定位的精度。实验结果表明，与变分贝叶斯卡尔曼滤波和Sage-Husa滤波相比，无障碍情况下，基于改进算法的定位精度提高了6%以上；有障碍干扰下，则提高了14.6%以上。 |
| 关键词: 室内定位 移动目标 贝叶斯卡尔曼滤波 时变噪声 指纹滤波 |
| DOI： |
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| 基金项目:国家自然科学基金资助项目（61771265,61801247）；江苏省“青蓝工程”；南通市科技计划项目（CP12017001,GY12017006） |
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| Indoor moving target localization using time-varying noise Bayesian Kalman filter |
| YI Lihua,SHI Jiajia,XU Zhihuo,SHI Quan |
| (School of Information Science and Technoloy,Nantong University,Nantong 226019,China;School of Transportation and Civil Engineering,Nantong University,Nantong 226019,China;School of Information Science and Technoloy;b.School of Transportation and Civil Engineering,Nantong University,Nantong 226019,China) |
| Abstract: |
| The fingerprint filtering and positioning technology based on radio frequency identification(RFID) is one of the techniques commonly used in indoor positioning.For the problem of low positioning accuracy of the filtering algorithm based on RFID because it can not accurately adapt to the change of environmental noise,a Bayesian Kalman filter algorithm adapting to time-varying noise is proposed.The algorithm combines the Sage-Husa filtering model and the Bayesian model to optimize the process and measurement covariance matrix,thus effectively reducing the noise and improving the accuracy of fingerprint filtering positioning.The experimental results show that compared with the variable-decibel-siemen Kalman filter and Sage-Husa filter,the positioning accuracy based on the improved algorithm is increased by more than 6% under obstacle-free conditions,and the interference is improved by more than 14.6% under obstacle conditions. |
| Key words: indoor positioning moving target Bayesian Kalman filter time-varying noise fingerprint filtering |
《电讯技术》编辑部 