Modeling and performance analysis of indoor PDR based on multi-model fusion
(1. Jiangsu Province Key Laboratory of Broadband Wireless Communications and Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China; 2. School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China）
Abstract： To solve the problem that the pedestrian dead reckoning (PDR) indoor signals were susceptible to interference from environment and multipath effects, the optimal indoor PDR modelling method based on multi-model fusion was proposed. The system model of the multi-model fusion indoor PDR modelling approach was given with four key stages of step detection, step length projection, direction projection and position projection. In the step detection stage, the peak detection algorithm, local maximum algorithm and advance over zero detection algorithm were integrated, and in the step projection stage, the Weinberg method and Kim method were integrated. The Kalman filter algorithm was used to correct the errors of step detection and step projection. The comparison with traditional algorithms in terms of step number, step length, direction and position errors in different scenarios was completed. The results show that the fused model combines the feature recognition results of traditional step detection and step length estimation algorithms, which can realize the optimization of signal characteristics in the process of step detection and step length estimation. In the handheld scene, the step detection is accurate, and the step length estimation median error range is 0.060 m or less with the minimum direction estimation average absolute error of 3.06° and the position estimation average error of 0.235 3 m, which achieves good indoor walking status recognition and position estimation performance.
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