An improved particle filter indoor fusion positioning approach based on Wi-Fi/ PDR/ geomagnetic field

Wang, Tianfa; Han, Litao; Kong, Qi; Li, Zeyu; Li, Changsong; Han, Jingwei; Bai, Quan; Chen, Yanfei

doi:10.1016/j.dt.2023.03.021

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Although a single wireless positioning method has been well developed, it still has certain limitations [55]. The combination positioning method can fully consider the advantages of each single positioning method to design an effective fusion algorithm to improve the positioning accuracy.…”

Section: Combined Optimizationmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.

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Section: Combined Optimizationmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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“…In the navigation coordinate system, given the initial position of the pedestrian as 𝑥 , 𝑦 , through the analysis and calculation of acceleration and angular velocity, the initial heading angle 𝜑 and step length 𝑆𝐿 for the first step are obtained. Assuming that the heading angle remains constant within a single step, the coordinates of the pedestrian's next step can be calculated using formula (6).…”

Section: Pdr Pedestrian Locationmentioning

confidence: 99%

“…To address the limitations of inertial navigation, a Pedestrian Dead Reckoning (PDR) algorithm has been proposed, leveraging the characteristics of pedestrian movement. Wireless positioning technology encompasses various techniques such as Wi-Fi, Ultra-Wideband (UWB) [5] , and Bluetooth [6] . UWB-based localization exploits the penetration capability and high time resolution of UWB signals, theoretically achieving centimeter-level accuracy.…”

Section: Introductionmentioning

confidence: 99%

Indoor pedestrian fusion localization algorithm based on unscented Kalman filter for UWB/PDR

Zhan,

Gao,

Liu

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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In complex indoor environments, achieving satisfactory pedestrian localization using a single positioning technique proves challenging. For instance, Ultra-Wideband (UWB) positioning encounters non-line-of-sight errors in intricate indoor settings, while Pedestrian Dead Reckoning (PDR) technology with inertial sensors is susceptible to cumulative drift errors over time. Consequently, this paper introduces the Unscented Kalman Filter (UKF) algorithm to integrate UWB technology with PDR technology. The improved PDR-derived pedestrian localization information is employed as the state vector for the UKF algorithm, and the positioning information obtained through enhanced UWB technology serves as the observation vector for the UKF algorithm. This combined approach effectively corrects pedestrian position information, ultimately yielding more accurate pedestrian locations. Research results indicate that the proposed algorithm achieves a root mean square error of 3.64 centimeters. In comparison to a standalone UWB algorithm, this method demonstrates superior positioning accuracy in complex environments.

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