Smartphone Based Indoor Path Estimation and Localization Without Human Intervention

Choi, Junyoung; Lee, Gyujin; Choi, Sunghyun; Bahk, Saewoong

doi:10.1109/tmc.2020.3013113

Cited by 23 publications

(5 citation statements)

References 52 publications

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“…Due to the variety of indoor positioning scenes, various indoor localization solutions based on multiple tools such as WiFi [3][4][5], Bluetooth [6][7][8], UWB [9,10], RFID [11], INS [12], infrared [13], and magnetic field [14] have been developed. Infrastructure-based indoor positioning technologies include RFID, Bluetooth, infrared, and UWB.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

Liu

Zhang

et al. 2022

Journal of Sensors

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This paper presents a magnetic matching-aided indoor localization system based on a waist-mounted self-contained sensor array. Our purpose is to localize and track the elderly in nursing homes through the proposed wearable device to ensure their safety. The device consists of a low-cost 9-axis self-contained sensor array, a microcontroller, and a WiFi transmission module. This system uses the step length and heading-based pedestrian dead reckoning (PDR) framework as the backbone to estimate the user’s position using the averaged inertial data from the sensor array. A magnetic fingerprint matching (MFM) algorithm is introduced to constrain the drift of the PDR system. Meanwhile, we construct a single-step-based hybrid magnetic fingerprint model to improve the low discernibility of the magnetic field. Finally, we propose an augmented particle filter to fuse the PDR and the MFM algorithms to enhance the system performance further. Experimental results show that 95% of the positioning error after fusion is about 1.47 m, which is 99.3% higher than that of PDR, and the average positioning error after fusion is 0.55 m, which is 61.3% higher than that of PDR. Experimental results have successfully validated the effectiveness and high performance of the proposed magnetic matching-aided wearable indoor localization system.

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Section: Introductionmentioning

confidence: 99%

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

Liu

Zhang

et al. 2022

Journal of Sensors

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“…Otherwise, the positioning result is outputted after adaptive particle filtering. In [58], the attenuation of RSSI is more pronounced when passing through room doors, which are used as landmarks in the indoor environment. By accurately monitoring the time when users pass through the doors, this compensates for errors in the cumulative inertial measurement unit (IMU) data so as to achieve the goal of improving positioning accuracy.…”

Section: Combined With Inertial Sensormentioning

confidence: 99%

Research Progress of Wireless Positioning Methods Based on RSSI

Chen,

Ma,

Zhang

et al. 2024

Electronics

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Location-based services are now playing an integral role in the development of emerging industries, such as the Internet of Things, artificial intelligence and smart cities. Although GPS, Beidou and other satellite positioning technologies are becoming more and more mature, they still have certain limitations. In order to meet the needs of high-precision positioning, wireless positioning is proposed as a supplementary technology to satellite positioning, in which the Received Signal Strength Indication (RSSI) is one of the most popular positioning methods. In this paper, the application scenarios, evaluation methods and related localization methods of wireless positioning based on RSSI are studied. Secondly, the relevant optimization methods are analyzed and compared from different angles, and the methods of RSSI data acquisition are described. Finally, the existing problems and future development trends in RSSI positioning methods are expounded, which has certain reference significance for further research on RSSI localization.

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“…Theoretically, fingerprinting techniques are capable of localizing using a single beacon, and would fall into this latter group; however, most practical applications use multiple beacons [10] or fuse them with other technologies [86]. This is because of the ability of DF to maximize the advantages of technologies while minimizing their negatives [95], such as reducing an IPS sensitivity to RSS fluctuations and providing landmarks to reduce the error drift in PDR.…”

Section: Data Fusionmentioning

confidence: 99%

On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies

Leitch,

Ahmed,

Abbas

et al. 2023

Sensors

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Indoor localization is a key research area and has been stated as a major goal for Sixth Generation (6G) communications. Indoor localization faces many challenges, such as harsh wireless propagation channels, cluttered and dynamic environments, non-line-of-sight conditions, etc. There are various technologies that can be applied to address these issues. In this paper, four major technologies for implementing an indoor localization system are reviewed: Wireless Fidelity (Wi-Fi), Ultra-Wide Bandwidth Radio (UWB), Bluetooth Low Energy (BLE), and Inertial Measurement Units (IMU). Sections on Data Fusion (DF) and Machine Learning (ML) have been included as well due to their key role in Indoor Positioning Systems (IPS). These technologies have been categorized based on the techniques that they employ and the associated errors in localization. A brief comparison between these technologies is made based on specific performance metrics. Finally, the limitations of these techniques are identified to aid future research.

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Smartphone Based Indoor Path Estimation and Localization Without Human Intervention

Cited by 23 publications

References 52 publications

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

Magnetic Matching-Aided Indoor Localization System Based on a Waist-Mounted Self-Contained Sensor Array

Research Progress of Wireless Positioning Methods Based on RSSI

On Indoor Localization Using WiFi, BLE, UWB, and IMU Technologies

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