Recent advances in floor positioning based on smartphone

Wang, Qu; Fu, Meixia; Wang, Jianquan; Luo, Haiyong; Sun, Lei; Ma, Zhangchao; Li, Wei; Zhang, Chaoyi; Huang, Rong; Li, Xianda; Jiang, Zhuqing; Huang, Yan; Xia, Ming

doi:10.1016/j.measurement.2023.112813

Cited by 9 publications

(2 citation statements)

References 135 publications

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“…A network-based mobile positioning system using an optimization model … (Ahmad Sabri) 301 in (3) maps a round surface into a flat rectangular grid while maintaining size, shape, and area. Compared to harvesine for calculating small distances, the equirectangular formula gives a smaller relative mean of error [28].…”

Section: Methodsmentioning

confidence: 99%

A network-based mobile positioning system using an optimization model

Sabri,

Kosasih

2024

IJAAS

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The expansion of cellular network coverage facilitates the advancement of research on network-based positioning. We are interested in the signal fingerprinting method to predict the location of a mobile device. By this method, the device must be within the fingerprint coverage to have a successful location prediction. However, any disturbance in the signal propagation would decrease the prediction accuracy. We propose an optimization model based on generalized triangulation combined with a signal fingerprint which is treated more adaptively in responding to any signal disturbance. The triangulation method determines the most likely region where the device is located. The solution provides the estimated longitude and latitude of the device. An illustration of the implementation of the model is presented. The model is assessed using the Indosat cellular network in three distinct testbeds in Indonesia, which are: South Jakarta, a metropolitan area; South Tangerang, a buffer area adjacent to the metropolitan area; and Malang, a city surrounded by rural areas. The most favorable outcome yields an average prediction error of 39.6 m, a maximum error of 197.08 m, a minimum error of 0.05 m, and a standard deviation of error of 39.22 m.

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Section: Methodsmentioning

confidence: 99%

A network-based mobile positioning system using an optimization model

Sabri,

Kosasih

2024

IJAAS

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“…The authors of the article 22 point out that although air pressure difference measurements are the basis for floor positioning methods, these methods encounter challenges. First, these methods depend on reference barometers distributed throughout the environment, which limits their scalability.…”

Section: State Of the Artmentioning

confidence: 99%

A comprehensive algorithm for vertical positioning in multi-building environments as an advancement in indoor floor-level detection

Marjasz,

Grochla,

Połys

2024

Sci Rep

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The proliferation of smartphones has catalyzed diverse services, mainly focusing on indoor localization to determine users’ and devices’ positions within buildings. Despite decades of exploration, the seamless integration of wireless technologies in tracking devices and users has become pivotal in various sectors, including health, industry, disaster management, building operations, and surveillance. Extensive research in laboratory and industrial settings, particularly in wireless sensor networks and robotics, has informed indoor localization techniques. This paper, referencing surveys and original literature reviews, proposes an innovative indoor location system amalgamating GPS and barometer readings. GPS identifies entry through doors, while barometer readings facilitate accurate floor-level tracking. The integration promises continuous real-time location updates, enhancing security, navigation, and emergency response. Notably, the algorithm is infrastructure-independent, relying on the smartphone’s barometer, and versatile, detecting elevator travel when Wi-Fi AP or LTE signals are available. Results indicate high accuracy, with building entry exceeding 93%, elevator recognition achieving 75% sensitivity and 97% specificity, and floor change detection surpassing 95% sensitivity and nearly 98% specificity (which translates to nearly 97% accuracy). This comprehensive solution, emphasizing the critical role of precise vertical positioning, signifies an advancement in tracking within urban structures.

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Indoor Floor Detection and Localization Based on Deep Learning and Particle Filter

Lin,

Shin

2024

Navigation: Science and Technology

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Recent advances in floor positioning based on smartphone

Cited by 9 publications

References 135 publications

A network-based mobile positioning system using an optimization model

A network-based mobile positioning system using an optimization model

A comprehensive algorithm for vertical positioning in multi-building environments as an advancement in indoor floor-level detection

Indoor Floor Detection and Localization Based on Deep Learning and Particle Filter

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