Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update

Wei, Ran; Xu, Hongda; Yang, Mingkun; Yu, Xinguo; Xiao, Zhenyu; Yan, Bo

doi:10.3390/s21113808

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…The infrastructure-free PDR calibration method reduces error through algorithmic process. A representative method is zero-velocity update algorithm (ZUPT) [13,14]. However, it is inapplicable for practical conditions since it is generally suited for foot-based dead reckoning systems, which require specially designed shoes or insoles to secure the inertial sensor.…”

Section: Introductionmentioning

confidence: 99%

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

Chen,

Xie,

Zhou

et al. 2024

Preprint

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Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

show abstract

Section: Introductionmentioning

confidence: 99%

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

Chen,

Xie,

Zhou

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The infrastructure-free PDR calibration method reduces errors through an algorithmic process. A representative method is the zero-velocity update algorithm (ZUPT) [13,14], which is generally suited for foot-based dead reckoning systems. However, Yu et al proposed that an IMU installed on the feet may be influenced by impacts between the feet and the ground during walking [15].…”

Section: Introductionmentioning

confidence: 99%

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

Chen,

Xie,

Zhou

et al. 2024

Electronics

View full text Add to dashboard Cite

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated errors in multi-floor scenes. The proposed calibration method involves two steps: motion pattern recognition and position matching-based calibration. The motion pattern recognition aims to detect different motion patterns, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion patterns and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR, achieving accurate 3D positioning. The average error is reduced from 6.60 m to 1.37 m.

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Enhanced WiFi/Pedestrian Dead Reckoning Indoor Localization Using Artemisinin Optimization-Particle Swarm Optimization-Particle Filter

Liu,

Song,

Chen

et al. 2024

Electronics

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WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively.

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Real-Time Pedestrian Tracking Terminal Based on Adaptive Zero Velocity Update

Cited by 5 publications

References 34 publications

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

An Indoor 3D Positioning Method Using Terrain Feature Matching for PDR Error Calibration

Enhanced WiFi/Pedestrian Dead Reckoning Indoor Localization Using Artemisinin Optimization-Particle Swarm Optimization-Particle Filter

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