Abstract
In recent years, the public’s demand for location services has increased significantly. As outdoor positioning has matured, indoor positioning has become a focus area for researchers. Various indoor positioning methods have emerged. Pedestrian dead reckoning (PDR) has become a research hotspot since it does not require a positioning infrastructure. An integral equation is used in PDR positioning; thus, errors accumulate during long‐term operation. To eliminate the accumulated errors in PDR localisation, this paper proposes a PDR localisation system applied to complex scenarios with multiple buildings and large areas. The system is based on the pedestrian movement behavior recognition algorithm proposed in this paper, which recognises the behavior of pedestrians for each gait and improves the stride length estimation for PDR localisation based on the recognition results to reduce the accumulation of errors in the PDR localisation algorithm itself. At the same time, the system uses self‐researched hardware to modify the audio equipment used for broadcasting within the indoor environment, to locate the acoustic source through a Hamming distance‐based localisation algorithm, and to correct the estimated acoustic source estimated location based on the known source location in order to eliminate the accumulated error in PDR localisation. Through analysis and experimental verification, the recognition accuracy of pedestrian movement behavior recognition proposed in this paper reaches 95% and the acoustic source localisation accuracy of 0.32 m during movement, thus, producing an excellent effect on eliminating the cumulative error of PDR localisation.
Highlights
In recent years, the public’s demand for location-based services (LBS) has become more robust, and LBS has affected many aspects of people’s work and life
(3) We propose a method to improve the accuracy of Pedestrian dead reckoning (PDR) localisation using pedestrian movement behavior recognition
The method uses the proposed method based on gait periodicity features proposed in this paper to extract features from the data collected by smartphones, uses support vector machine (SVM) as a classifier to recognize the movement behavior of each gait, and uses Dempster–Shafer (D-S) evidence theory to fuse the recognition results for the problem of low recognition accuracy in certain complex scenes, improving the overall recognition accuracy to 96%
Summary
The public’s demand for location-based services (LBS) has become more robust, and LBS has affected many aspects of people’s work and life. Positioning infrastructure is required, which increases and is not conducive to largescale applications Another solution is to reduce the cumulative error using step detection, stride length estimation, and heading determination to reduce the impact of noise [16]. The method introduces pedestrian movement behavior recognition to improve PDR localisation accuracy, while using acoustic source localisation to reverse the cumulative error of PDR localisation (2) We propose an acoustic localisation algorithm based on Hamming distance. Use the known position of the sound source to correct the estimated position, in order to achieve the purpose of eliminating the accumulated error of PDR positioning (3) We propose a method to improve the accuracy of PDR localisation using pedestrian movement behavior recognition.
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