The Improved Fingerprint-Based Indoor Localization with RFID/PDR/MM Technologies

Jie, Wu; Zhu, Muhua; Xiao, Bo; Qiu, Yunzhou

doi:10.1109/padsw.2018.8644602

Cited by 9 publications

(3 citation statements)

References 22 publications

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“…These approaches have their own advantages and limitations. Increasing numbers of studies focus on an automated and accurate fingerprinting method for indoor positioning and navigation systems [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. This section summarizes the main localization methods realized by users or by robots and presents a discussion of these methods.…”

Section: Related Workmentioning

confidence: 99%

“…The main references in the field of RSS-based fingerprinting are elaborated in [ 19 ]. Particularly, in [ 23 ], a novel crowd sourcing method is proposed for both radio map construction and updates to the map in order to reduce the site surveying time. Here, a fusion of RFID, Pedestrian Dead Reckoning (PDR) and Magnetic Matching (MM) technologies for indoor localization is presented.…”

Section: Related Workmentioning

confidence: 99%

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Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Hatem

Fortes

Colin

et al. 2021

Sensors

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Indoor localization is one of the most important topics in wireless navigation systems. The large number of applications that rely on indoor positioning makes advancements in this field important. Fingerprinting is a popular technique that is widely adopted and induces many important localization approaches. Recently, fingerprinting based on mobile robots has received increasing attention. This work focuses on presenting a simple, cost-effective and accurate auto-fingerprinting method for an indoor localization system based on Radio Frequency Identification (RFID) technology and using a two-wheeled robot. With this objective, an assessment of the robot’s navigation is performed in order to investigate its displacement errors and elaborate the required corrections. The latter are integrated in our proposed localization system, which is divided into two stages. From there, the auto-fingerprinting method is implemented while modeling the tag-reader link by the Dual One Slope with Second Order propagation Model (DOSSOM) for environmental calibration, within the offline stage. During the online stage, the robot’s position is estimated by applying DOSSOM followed by multilateration. Experimental localization results show that the proposed method provides a positioning error of 1.22 m at the cumulative distribution function of 90%, while operating with only four RFID active tags and an architecture with reduced complexity.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Hatem

Fortes

Colin

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Recently, the location-based service (LBS) for internet of things (IoT) becomes popular in many fields such as office building, shopping mall, and community building. Since the GPS system cannot catch satellite signals in an indoor environment, many indoor localization systems based on different RF signals have been presented, such as RFID [1][2][3], Bluetooth, and LoRa [4] based localization systems.…”

Section: Introductionmentioning

confidence: 99%

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

Chen

Hsu

Chung

2022

Sensors

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Indoor localization is an important issue for indoor location-based services. As opposed to the other indoor localization approaches, the radio frequency (RF) based approaches are low-energy solutions with simple implementation. The kernel learning has been used for the RF-based indoor localization in 2D environment. However, the kernel learning has not been used in 3D environment. Hence, this paper proposes a multi-kernel learning scheme for 3D indoor localization. Based on the signals collected in the area of interest, the WiFi signals with better quality and closer to the user are selected so as to reduce the multipath effect and the external interference. Through the construction of multi-kernel, the localization accuracy can be improved as opposed to the localization based on the single kernel. We build multiple kernels to get the user’s location by collecting wireless received signal strengths (RSS) and signal-to-noise ratios (SNR). The kernel learning maps data to high dimension space and uses the optimization process to find the surface where the data are mapped. By multi-kernel training, the surface is fine-tuned and eventually converges to form the location database during the mapping process. The proposed localization scheme is verified by the real RSS and SNR collected from multiple wireless access points (AP) in a building. The experimental results verify that the proposed multi-kernel learning scheme performs better than the multi-DNN scheme and the existing kernel-based localization schemes in terms of localization accuracy and error in 3D indoor environment.

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A Simulation Platform for Sensing System Selection for Occupant Distribution Estimation in Smart Buildings

Tang

Jia

2019

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

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The Improved Fingerprint-Based Indoor Localization with RFID/PDR/MM Technologies

Cited by 9 publications

References 22 publications

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

Accurate and Low-Complexity Auto-Fingerprinting for Enhanced Reliability of Indoor Localization Systems

A Semi-Supervised 3D Indoor Localization Using Multi-Kernel Learning for WiFi Networks

A Simulation Platform for Sensing System Selection for Occupant Distribution Estimation in Smart Buildings

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