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An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter
EURASIP Journal on Wireless Communications and Networking ( IF 2.6 ) Pub Date : 2020-12-02 , DOI: 10.1186/s13638-020-01809-y
Haiyun Yao , Hong Shu , Hongxing Sun , B. G. Mousa , Zhenghang Jiao , Yingbo Suo

Indoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.



中文翻译:

基于无味卡尔曼滤波器的WiFi / PDR /智能手机集成室内定位系统完整性监控算法

室内定位导航技术发展迅速,但在行人航位推算(PDR)和WiFi室内定位导航系统中的完整性监控方面投入很少。PDR准确性会随时间推移而漂移。同时,由于严重的多径传播以及人们四处走动时对信号的干扰,在复杂的室内环境中,WiFi定位精度会降低。在我们的研究中,我们旨在通过基于无味卡尔曼滤波器(UKF)的集成WiFi / PDR的室内定位系统的完整性监控算法来提高定位质量。完整性监控分为三个阶段。基于UKF创新的测试统计数据确定定位系统是否异常。一旦检测到定位系统异常,触发强大的UKF(RUKF)以实现更高的定位精度。再次,RUKF的创新被用于判断观测值中的异常值并识别定位系统故障。在最后的完整性监视阶段,将及时向用户发出警报,以减少定位错误的风险。我们进行了仿真,以分析完整性监控的计算复杂性。结果表明,当系统中状态向量和观察向量的维数较小时(<20),它不会显着增加总体计算复杂度。实际上,室内定位系统中状态向量和观测向量的维数很少超过20。在两次现场实验中对提出的完整性监控算法进行了测试,结果表明,该算法非常健壮,

更新日期:2020-12-02
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