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余成波,成科宏. WiFi与行人航迹推算自适应无迹卡尔曼滤波融合定位算法[J]. 科学技术与工程, 2020, 20(27): 11155-11160.
YU Cheng-bo,CHENG Ke-hong.WiFi and PDR adaptive UKF fusion positioning algorithm[J].Science Technology and Engineering,2020,20(27):11155-11160.
WiFi与行人航迹推算自适应无迹卡尔曼滤波融合定位算法
WiFi and PDR adaptive UKF fusion positioning algorithm
投稿时间:2019-06-13  修订日期:2020-06-23
DOI:
中文关键词:  wifi室内定位  行人航位推算 自适应调整因子  无迹卡尔曼滤波
英文关键词:wifi indoor positioning  pedestrian dead reckoning  adaptive adjustment factor  unscented kalman filte
基金项目:国家高端外国专家项目“智能车辆中的5G无线通信网络关键应用技术(GDW20185200480)”;重庆市科技人才培养计划(新产品研发团队)资助项目(CSJC2013KJRC-TDJS40012);重庆市高校优秀成果转化资助项目(KJZH14213)
     
作者单位
余成波 重庆理工大学 远程测试与控制研究所 重庆市 400054
成科宏 重庆理工大学 远程测试与控制研究所 重庆市 400054
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中文摘要:
      针对wifi信号易受环境波动和存在多径效应现象,行人航位推算(PDR)系统中传感器模块精度有限,且存在累计误差问题,因此提出了一种加入自适应调整因子的改进无迹卡尔曼滤波(UKF)融合算法,该算法利用残差rk的理论协方差与实际协方差的差异作为条件,引入调整因子ρ调整状态向量和观测向量的协方差进而调整卡尔曼增益参数,增强系统鲁棒性和提高定位精度。在实验环境中,实验结果表明所提算法的平均误差为1.35m,最大误差为2.23m。定位位置误差在1.5m以内的概率达到了约80%,相比于标准UKF算法在1.5m以内的概率约为22%,提高了约58%。
英文摘要:
      In view of the susceptibility of wifi signals to environmental fluctuations and multipath phenomena, the accuracy of sensor modules in pedestrian dead reckoning (PDR) systems is limited and there is cumulative error. An improved unscented Kalman filter (UKF) with adaptive adjustment factor is proposed. The fusion algorithm uses the difference between the theoretical covariance and the actual covariance of the residual rk as a condition to introduce the adjustment factor ρ to adjust the state vector and the observed covariance to adjust the Kalman gain parameter, which increases the system robustness and improves the accuracy. In the experimental environment, the experimental results show that the proposed algorithm has an average error of 1.35m and a maximum error of 2.23m. The probability of positioning position error within 1.5m is about 80%, and the probability of being within 1.5m compared to the standard UKF algorithm is about 22%. The proposed algorithm is increased by about 58%.
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