Two-stage clustering for improve indoor positioning accuracy

Lin, Huang; Purmehdi, Hakimeh; Fei, Xiaoning; Zhao, Yuxin; Isac, Alka; Louafi, Habib; Peng, Wei

doi:10.1016/j.autcon.2023.104981

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Due to the complexity of RSSI measurement and collection, data sets are usually unevenly distributed, which is mainly manifested in distant points have similar RSSI values [48], which is one of the main reasons for inaccurate positioning of database matching methods. The optimization of database matching method is usually divided into offline database establishment optimization and online database matching optimization.…”

Section: Optimization Based On Database Matching Methodsmentioning

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: Optimization Based On Database Matching Methodsmentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

An improved pedestrian dead reckoning method based on gait pattern recognition

Liu,

Cui,

2024

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Purpose The pedestrian dead reckoning (PDR) based on smartphones has been widely applied in continuous indoor positioning. However, when the position of the mobile phone and the walking patterns of the pedestrian are mixed, traditional PDR tends to become confused and thus degrade performance. To address this issue, this paper aims to propose an improved PDR scheme by focusing on gait pattern recognition and the impact of short-period but negative transitions on tracking. Design/methodology/approach The overall solution uses the inertial sensor integrated within the phone for positioning. A binary classifier-based change point detection algorithm is used to identify the transition points in pedestrian gait. Additionally, to enhance the accuracy of gait recognition, this paper presents a combined CNN-attention-based bi-directional long short-term memory(ABiLSTM) model, integrating convolutional neural networks (CNN), bi-directional long short-term memory (Bi-LSTM) and an attention mechanism, to recognize the current gait pattern. The outcomes of this gait pattern recognition are then applied to PDR. Based on distinct gait patterns, corresponding PDR strategies are devised to enable continuous tracking and positioning of pedestrians. Findings Through experimental verification, the CNN-ABiLSTM model achieves a gait recognition accuracy of 99.52% on the self-constructed data set. The pedestrian navigation estimation method proposed in this paper, which is based on gait recognition assistance, demonstrates a 32.56% improvement in accuracy over traditional positioning algorithms in multi-gait scenarios. Originality/value The improved PDR scheme algorithm significantly enhances the robustness and smoothness of pedestrian tracking, particularly during multiple gait transitions. This, in turn, provides strong support for the utilization of low-cost inertial sensors integrated within mobile phones for indoor positioning applications.

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Two-stage clustering for improve indoor positioning accuracy

Cited by 2 publications

References 17 publications

Research Progress of Wireless Positioning Methods Based on RSSI

Research Progress of Wireless Positioning Methods Based on RSSI

An improved pedestrian dead reckoning method based on gait pattern recognition

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