Locating Smartphones Indoors Using Built-In Sensors and Wi-Fi Ranging With an Enhanced Particle Filter

Xu, Shihao; Chen, Ruizhi; Yu, Yue; Guo, Guangyi; Huang, Lixiong

doi:10.1109/access.2019.2927387

Cited by 81 publications

(66 citation statements)

References 36 publications

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“…Particles with low weight are then discarded, while new particles are sampled to keep the overall number of particles at a desired value. Particle filers have been applied to this problem as well [35].…”

Section: Particle Filtermentioning

confidence: 99%

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

Horn¹

2020

Sensors

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Determination of indoor position based on fine time measurement (FTM) of the round trip time (RTT) of a signal between an initiator (smartphone) and a responder (Wi-Fi access point) enables a number of applications. However, the accuracy currently attainable-standard deviations of 1-2 m in distance measurement under favorable circumstances-limits the range of possible applications. An emergency worker, for example, may not be able to unequivocally determine on which floor someone in need of help is in a multi-story building. The error in position depends on several factors, including the bandwidth of the RF signal, delay of the signal due to the high relative permittivity of construction materials, and the geometry-dependent "noise gain" of position determination. Errors in distance measurements have unusal properties that are exposed here. Improvements in accuracy depend on understanding all of these error sources. This paper introduces "frequency diversity," a method for doubling the accuracy of indoor position determination using weighted averages of measurements with uncorrelated errors obtained in different channels. The properties of this method are verified experimentally with a range of responders. Finally, different ways of using the distance measurements to determine indoor position are discussed and the Bayesian grid update method shown to be more useful than others, given the non-Gaussian nature of the measurement errors.

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Section: Particle Filtermentioning

confidence: 99%

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

Horn¹

2020

Sensors

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“…The unscented Kalman filter (UKF) was finally utilized to fuse the robust dead reckoning and Wi-Fi FTM. Their experimental results showed that the mean positioning errors were within 2 m. Xu et al [38] provided two different strategies to update the particle set in the enhanced particle filter and employed it to combine the PDR and Wi-Fi FTM. The experimental results indicated that the mean positioning accuracy was approximately 1 m, and the new position was given within 0.5 second.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

Sun

Wang

et al. 2020

IEEE Access

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The smartphone-based Wi-Fi fine time measurement (FTM) technique has provided a new approach for Wi-Fi-based indoor location on mobile phones since the 2018 release of the Google Android Pie system, which supports the IEEE 802.11-2016 protocol and can directly measure the distance between the initiator and the responder. This paper studies in detail the properties of mobile phone Wi-Fi ranging and positioning performance. Considering non-line-of-sight (NLOS) error identification, a real-time ranging error compensation model based on the least-squares (LS) method and an adaptive Wi-Fi FTM positioning algorithm (AWFP) utilizing the weighted least-squares (WLS) method are devised. To improve accuracy, a new tightly coupled fusion positioning algorithm integrating Wi-Fi FTM and built-in mobile phone sensors based on the extended Kalman filter (EKF) is constructed. The experimental results show that the ranging precision and Wi-Fi positioning accuracy are improved. Based on the high-precision Wi-Fi ranging and positioning results, the final location accuracy of the proposed fusion method is 0.98 m, and the root-meansquare error (RMSE) is 1.10 m, which are better than those of the PDR, Wi-Fi FTM and loosely coupled PDR/Wi-Fi FTM integration based on the EKF. INDEX TERMS Indoor positioning, Wi-Fi FTM, pedestrian dead reckoning, tightly coupled, ekf.

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“…A mobile application relying on built-in smartphone sensors that emulates a pedestrian dead reckoning system with adaptive tilt compensation to estimate accurate headings in various linear displacements has been detailed by Xu et al [11].…”

Section: Current Contextmentioning

confidence: 99%

Sensors for Web – Alternative for Traditional Mobile Applications

Obretin¹

2020

IJISRT

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This paper is providing an in-detail analysis of existing mobile based software solutions for indoor localization and identifies several issues to be tackled. The objective this research initiative is assuming is to provide an alternative approach for traditional sensorbased applications, an alternative that behaves better in terms of resource consumption, maintenance costs and degree of generality. The principles hereby described have been integrated into a web application that has been tested in two different environments, providing indoor localization accuracy under a meter

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Locating Smartphones Indoors Using Built-In Sensors and Wi-Fi Ranging With an Enhanced Particle Filter

Cited by 81 publications

References 36 publications

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

Doubling the Accuracy of Indoor Positioning: Frequency Diversity

Indoor Positioning Tightly Coupled Wi-Fi FTM Ranging and PDR Based on the Extended Kalman Filter for Smartphones

Sensors for Web – Alternative for Traditional Mobile Applications

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