Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks

Deng, Zhongliang; Wang, Guofeng; Qin, Danyang; Na, Zhenyu; Cui, Yang; Chen, Juan

doi:10.3390/s16091427

Cited by 43 publications

(38 citation statements)

References 34 publications

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“…Experiment results are illustrated and analyzed, and a comparison is made between PDR and LaP. We also make a comparison among LaP and two kinds of WiFi, and PDR and floor plan fusion: in the following, we will use the abbreviations PF-based [21] and EKF-based [22] to indicate the two approaches, respectively. The reason why the WPL-based approach [20] is not considered in this section is that we find it difficult to obtain an accurate pass-loss model of this indoor environment.…”

Section: Discussionmentioning

confidence: 99%

“…One of the most recent works can be found in the work of Deng et al [22], in which, similar to other works, the authors also utilized WiFi, PDR and a floor plan at the same time. The innovation was that they used an extended Kalman filter (EKF) twice, and during the WiFi positioning phase, they adopted a kernel density estimation (KDE) model.…”

Section: Related Workmentioning

confidence: 99%

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An Indoor Positioning Method for Smartphones Using Landmarks and PDR

Wang

Jiang

Guo

et al. 2016

Sensors

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Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

Wang

Jiang

Guo

et al. 2016

Sensors

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show abstract

“…In recent years, technological advances have been made in smartphone uses. Almost all smartphones available in the market have embedded sensors such as GPS, accelerometers, gyroscopes, magnetometers, proximity sensors and barometers [1]. Having these inertial sensors makes it possible to determine movement and orientation in three axes.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019

IJRTE

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This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

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“…A variety of sensor technologies could be utilized as a medium to determine indoor location. Radio Frequency (RF) signals such as ultrasound [1], Bluetooth [2], radio frequency identification (RFID) [3], infrared [4], ultra-wide band (UWB) [5], ZigBee [6], and Wi-Fi [7] have been used in the past and present of LBS and indoor localization work. Among those mentioned, Wi-Fi signals using received signal strength (RSS) fingerprinting has been considered as one of the most popular indoor positioning solutions due to its low cost [8].…”

Section: Introductionmentioning

confidence: 99%

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting

Husen

Lee

2016

Sensors

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A method of location fingerprinting based on the Wi-Fi received signal strength (RSS) in an indoor environment is presented. The method aims to overcome the RSS instability due to varying channel disturbances in time by introducing the concept of invariant RSS statistics. The invariant RSS statistics represent here the RSS distributions collected at individual calibration locations under minimal random spatiotemporal disturbances in time. The invariant RSS statistics thus collected serve as the reference pattern classes for fingerprinting. Fingerprinting is carried out at an unknown location by identifying the reference pattern class that maximally supports the spontaneous RSS sensed from individual Wi-Fi sources. A design guideline is also presented as a rule of thumb for estimating the number of Wi-Fi signal sources required to be available for any given number of calibration locations under a certain level of random spatiotemporal disturbances. Experimental results show that the proposed method not only provides 17% higher success rate than conventional ones but also removes the need for recalibration. Furthermore, the resolution is shown finer by 40% with the execution time more than an order of magnitude faster than the conventional methods. These results are also backed up by theoretical analysis.

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Continuous Indoor Positioning Fusing WiFi, Smartphone Sensors and Landmarks

Cited by 43 publications

References 34 publications

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting

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