A Precise Dead Reckoning Algorithm Based on Bluetooth and Multiple Sensors

Yu, Ning; Zhan, Xi; Zhao, Shengnan; Yue, Wu; Ran, Feng

doi:10.1109/jiot.2017.2784386

Cited by 97 publications

(39 citation statements)

References 27 publications

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“…In [55], Dijkstra's shortest path algorithm is used to find the shortest route for navigation. Yu et al [56] propose a dead reckoning algorithm that combines Bluetooth and multiple sensors to improve localization accuracy. This algorithm includes multisensor-based position estimation, Bluetooth model-based position estimation, and Kalman filter fusion.…”

Section: Triangulation Algorithmsmentioning

confidence: 99%

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Khanh

Nguyen

Pham

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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Increasing demands for location-based services require accurate wireless indoor location information. Location-based services include indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [1]. Additionally, such services are becoming essential in various other fields such as mobile commerce, parcel or vehicle tracking, discovering the nearest shops or restaurants, and social

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Section: Triangulation Algorithmsmentioning

confidence: 99%

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Khanh

Nguyen

Pham

et al. 2020

Hum. Cent. Comput. Inf. Sci.

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“…The original data from the accelerometer contains noise which should be smoothed. A moving average filter can be used to reduce the noise, and the obvious step character can be extracted after filtering [21].…”

Section: Multi-pattern-based Step Detection and Step-length Estimationmentioning

confidence: 99%

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Chen

et al. 2019

Remote Sensing

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More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

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“…To improve the precision and adaptability, Subha [30] and Ali [25] proposed adding particular terms to the original model to absorb the multipath error. Yu [31] put forward a new BLE propagation model according to the location of the BLE beacons and fused the propagation model and multiple sensors through the Kalman filter. Moreover, Canton Paterna [32] used frequency diversity, the Kalman filter and a trilateration method to improve the precision of the BLE system.…”

Section: Related Workmentioning

confidence: 99%

A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information

Huang

Liu

Sun

et al. 2019

Sensors

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Among the current indoor positioning technologies, Bluetooth low energy (BLE) has gained increasing attention. In particular, the traditional distance estimation derived from aggregate RSS and signal-attenuation models is generally unstable because of the complicated interference in indoor environments. To improve the adaptability and robustness of the BLE positioning system, we propose making full use of the three separate channels of BLE instead of their combination, which has generally been used before. In the first step, three signal-attenuation models are separately established for each BLE advertising channel in the offline phase, and a more stable distance in the online phase can be acquired by assembling measurements from all three channels with the distance decision strategy. Subsequently, a weighted trilateration method with uncertainties related to the distances derived in the first step is proposed to determine the user’s optimal position. The test results demonstrate that our proposed algorithm for determining the distance error achieves a value of less than 2.2 m at 90%, while for the positioning error, it achieves a value of less than 2.4 m at 90%. Compared with the traditional methods, the positioning error of our method is reduced by 33% to 38% for different smartphones and scenarios.

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A Precise Dead Reckoning Algorithm Based on Bluetooth and Multiple Sensors

Cited by 97 publications

References 27 publications

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

Wi-Fi indoor positioning and navigation: a cloudlet-based cloud computing approach

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information

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