A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors

Li, You; Zhuang, Yuan; Lan, Haiyu; Zhou, Qifan; Niu, Xiaoji; El‐Sheimy, Naser

doi:10.1109/lcomm.2015.2496940

Cited by 95 publications

(44 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous work [1], [3] in similar scale environments achieved a positioning error distance of around 6 metres; however, our system is capable of providing a more accurate positioning service.…”

Section: Introductionmentioning

confidence: 80%

Subway station real-time indoor positioning system for cell phones

Wan

Chau

et al. 2017

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 80%

Subway station real-time indoor positioning system for cell phones

Wan

Chau

et al. 2017

2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN)

View full text Add to dashboard Cite

“…In [29], an integrated technique for merging Wi-Fi localization system, PDR and smartphone sensor data using a UKF algorithm for 3D indoor localization is proposed. In [30,31], a detailed analysis of sensor fusion frameworks of Wi-Fi fingerprint with PDR systems are discussed and the results indicate that a hybrid localization system's performance is better than that of individual localization systems. An adaptive and robust filter for indoor localization using Wi-Fi and PDR is proposed by Li et al [32].…”

Section: Related Workmentioning

confidence: 99%

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

2019

View full text Add to dashboard Cite

Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion.

show abstract

“…However, the system had only one UWB beacon node, and only UWB was used to correct the cumulative error of PDR. In Reference [27], the core idea is mainly to correct the PDR direction, but the parameters cannot be adjusted for each pedestrian. Qian proposes a fusion method for WiFi/magnetic matching/PDR in Reference [28], but the "fingerprinting"-based algorithm limits its application for harsh environments.…”

Section: Hybrid Systemmentioning

confidence: 99%

A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Tong

Ning

et al. 2019

Sensors

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) and the Internet of Things (IoT) have been widely used in industrial, construction, and other fields. In recent years, demands for pedestrian localization have been increasing rapidly. In most cases, these applications work in harsh indoor environments, which have posed many challenges in achieving high-precision localization. Ultra-wide band (UWB)-based localization systems and pedestrian dead reckoning (PDR) algorithms are popular. However, both have their own advantages and disadvantages, and both exhibit a poor performance in harsh environments. UWB-based localization algorithms can be seriously interfered by non-line-of-sight (NLoS) propagation, and PDR algorithms display a cumulative error. For ensuring the accuracy of indoor localization in harsh environments, a hybrid localization approach is proposed in this paper. Firstly, UWB signals cannot penetrate obstacles in most cases, and traditional algorithms for improving the accuracy by NLoS identification and mitigation cannot work in this situation. Therefore, in this study, we focus on integrating a PDR and UWB-based localization algorithm according to the UWB communication status. Secondly, we propose an adaptive PDR algorithm. UWB technology can provide high-precision location results in line-of-sight (LoS) propagation. Based on these, we can train the parameters of the PDR algorithm for every pedestrian, to improve the accuracy. Finally, we implement this hybrid localization approach in a hardware platform and experiment with it in an environment similar to industry or construction. The experimental results show a better accuracy than traditional UWB and PDR approaches in harsh environments.Sensors 2020, 20, 193 2 of 20 most important feature of harsh environments. Indoor localization technology is still very challenging under NLoS propagation in harsh environments.The current mainstream indoor localization systems are radio frequency (RF)-based. This kind of system basically requires beacon nodes, or beacon nodes (BNs), with known coordinates in the localization system. Each target to be located has an electronic tag, or tag node (TN). Specific features generated from the communication between the TN and BN are used for localization. These features mainly include the radio signal strength (RSS), time, signal angle, etc. The challenge of indoor localization systems in harsh environments is that these features can be severely disturbed [1] by NLoS propagation or other interference. As a result, the localization accuracy will be greatly reduced.In harsh environments, interference of an RF-based localization system can be mainly divided into three types. Firstly, multipath refraction is a common type of interference in harsh environments. It means that an RF signal will be transmitted to the receiver through multiple paths, which causes the transmission time to be unreliable and unpredictable RSS superposition. Additionally, it apparently has a greater impact on the signal angle. Secondly, non-line-of-sight (NLoS) propagati...

show abstract

A Hybrid WiFi/Magnetic Matching/PDR Approach for Indoor Navigation With Smartphone Sensors

Cited by 95 publications

References 12 publications

Subway station real-time indoor positioning system for cell phones

Subway station real-time indoor positioning system for cell phones

A Sensor Fusion Framework for Indoor Localization Using Smartphone Sensors and Wi-Fi RSSI Measurements

A Robust PDR/UWB Integrated Indoor Localization Approach for Pedestrians in Harsh Environments

Contact Info

Product

Resources

About