Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Zhuang, Yuan; El‐Sheimy, Naser

doi:10.1109/jsen.2015.2477444

Cited by 118 publications

(54 citation statements)

References 26 publications

(39 reference statements)

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“…For example, ToA and RSSI ranging using anchors can be combined with dead reckoning to improve localization as demonstrated in Refs. [29,30]. In the former, the authors used two-way time of arrival (TOA) for localization of vehicles in a vehicular network.…”

Section: Fusing Additional Sensor Datamentioning

confidence: 99%

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

Golestanian¹,

Siva²,

Poellabauer³

2017

Ad Hoc Networks

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The increasing importance of location-aware computing and context-dependent information has led to a growing interest in low-cost indoor positioning with submeter accuracy. Localization algorithms can be classified into range-based and range-free techniques. Additionally, localization algorithms are heavily influenced by the technology and network architecture utilized. Availability, cost, reliability and accuracy of localization are the most important parameters when selecting a localization method. In this chapter, we introduce basic localization techniques, discuss how they are implemented with radio frequency devices and then characterize the localization techniques based on the network architecture, utilized technologies and application of localization. We then investigate and address localization in indoor environments where the absence of global positioning system (GPS) and the presence of unique radio propagation properties make this problem one of the most challenging topics of localization in wireless networks. In particular, we study and review the previous work for indoor localization based on radio frequency (RF) signaling (like Bluetooth-based localization) to illustrate localization challenges and how some of them can be overcome.

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Section: Fusing Additional Sensor Datamentioning

confidence: 99%

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

Golestanian¹,

Siva²,

Poellabauer³

2017

Ad Hoc Networks

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“…So integrated navigation is a good way to combine various technologies to achieve the purpose of high accuracy and low cost at the same time. For the integration of WiFi and MEMS solutions, the literature [12] uses the robust extended Kalman filter (EKF) to fuse the information from WiFi/MEMS sensors in a multi-floor environment, while the research [13] presents a pedestrian navigator based on tightly coupled integration of low-cost MEMS sensors and WiFi for handheld devices. Using the same data from WiFi/MEMS sensors, various information fusion structures may lead to different results [3].…”

Section: Introductionmentioning

confidence: 99%

Two-level Indoor Navigation using WiFi and MEMS Sensors

Huang¹,

Zhang²,

Cui³

et al. 2018

Proceedings of the 2018 International Conference on Mathematics, Modelling, Simulation and Algorithms (MMSA 2018)

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Abstract-In this paper, the indoor pedestrian location algorithm is investigated, in which we proposes a novel WiFi/MEMS integration structure for indoor navigation to fuse the information from WiFi and MEMS sensors. In WiFi part, aiming at the problem of large amount of calculation in the algorithm, a partition solution called "moving partition" is originally proposed. The test results from indoor experiments indicate that with "moving partition", the computation time decreased by almost half and the proposed WiFi/MEMS solution works well, of which the mean positioning accuracy has been greatly improved compared with MEMS alone and WiFi alone.

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“…Indoor localization of mobile target is playing an increasingly important role on home intelligence, factory controllability, and shopping malls automation [1]. Indoor positioning technology is a critical part in the location based services for indoor mobile target.…”

Section: Introductionmentioning

confidence: 99%

Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

Yang

Luo

et al. 2017

Journal of Sensors

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According to the asynchronous transmission of data for the SINS/WSN integrated positioning system, this paper proposes a novel asynchronous data fusion method using Unscented Kalman Filter for SINS/WSN integrated positioning system based on indoor mobile target. The state equation of the integrated system is built with the motion characteristic of mobile target. The pseudo measurement equation is built based on the time sequence of SINS/WSN measured value through detecting the measurement of WSN in every fusion period. Considering that the improved state-space model, comprised of the state equation and pseudo measurement equation, is the nonlinear equations, the Unscented Kalman Filter is applied to estimate the state value of the statespace model. Hence the asynchronous data fusion method for SINS/WSN integrated positioning system can be achieved. Simulation results and experimental tests show that the positioning system with proposed asynchronous data fusion algorithm performs feasibility and stability under circumstances of the asynchronous time, and it is superior to the traditional asynchronous data fusion and synchronous data fusion methods.

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Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Cited by 118 publications

References 26 publications

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

Radio Frequency-Based Indoor Localization in Ad-Hoc Networks

Two-level Indoor Navigation using WiFi and MEMS Sensors

Integrated SINS/WSN Positioning System for Indoor Mobile Target Using Novel Asynchronous Data Fusion Method

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