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首页> 外文会议>IEEE International symposium on Personal, Indoor, and Mobile Radio Communications >A hybrid indoor positioning algorithm based on WiFi fingerprinting and pedestrian dead reckoning
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A hybrid indoor positioning algorithm based on WiFi fingerprinting and pedestrian dead reckoning

机译：基于WiFi指纹和行人航位推算的混合室内定位算法

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摘要

WiFi fingerprinting method is an attractive indoor positioning method due to widely deployed WiFi access points (APs) and easily measured received signal strength (RSS). However, WiFi fingerprinting positioning results are unstable because of the fluctuation of RSS. Besides, pedestrian dead reckoning (PDR) method relying on inertial sensors has been widely used in real-time tracking. Since PDR has accumulated errors in long distances tracking, this paper proposes a hybrid algorithm that integrates PDR approach with WiFi fingerprinting approach to further improve positioning accuracy. There are two key points in our algorithm. The first is utilizing dynamic subarea to restrict the searching region of WiFi fingerprinting method. The second is determining particular weights to fuse the positioning results of the above two approaches according to the distances between the current positioning results and the previous hybrid location. Further, we improve our hybrid algorithm which is based on the adjacent estimated positions of WiFi fingerprinting method. Experiment results showed that the average errors of our hybrid algorithm and improved hybrid algorithm were 2.22m and 1.64m respectively, which were reduced by 42% and 57% compared with the pure PDR method. Therefore, the proposed algorithms can provide stable and high positioning accuracy in real environment.
机译：WiFi指纹识别方法是一种有吸引力的室内定位方法，因为它广泛部署了WiFi接入点（AP）和易于测量的接收信号强度（RSS）。但是，由于RSS的波动，WiFi指纹定位结果不稳定。此外，依靠惯性传感器的行人航位推算（PDR）方法已被广泛用于实时跟踪。由于PDR在长距离跟踪中积累了误差，因此提出了一种混合算法，将PDR方法与WiFi指纹方法相集成，以进一步提高定位精度。我们的算法有两个关键点。首先是利用动态分区限制WiFi指纹识别方法的搜索区域。第二是根据当前定位结果与先前混合位置之间的距离，确定融合上述两种方法的定位结果的权重。此外，我们改进了基于WiFi指纹识别方法的相邻估计位置的混合算法。实验结果表明，我们的混合算法和改进的混合算法的平均误差分别为2.22m和1.64m，与纯PDR方法相比分别减少了42％和57％。因此，所提出的算法可以在实际环境中提供稳定且较高的定位精度。

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