A Survey of Machine Learning for Indoor Positioning

Nessa, Ahasanun; Adhikari, Bhagawat; Hussain, Fatima; Fernando, Xavier

doi:10.1109/access.2020.3039271

Cited by 171 publications

(108 citation statements)

References 137 publications

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“…Although several surveys (e.g. [20], [52] ) on localization are available, this section aims to compare the advantages and limitations of the system proposed here in relation to other similar systems. Table V summarizes some of the properties, advantages and disadvantages of the technologies studied.…”

Section: Discussionmentioning

confidence: 99%

“…Angle-of-arrival (AoA) localization techniques were recently investigated in [57] [58]. Fingerprinting is a widely used indoor positioning method used with several wireless access technologies including Wi-Fi, BLE, and Zigbee [52]. This method can improve accuracy in NLOS environments, but it requires the creation of a database from training measurements and the results depend on the density of reference points.…”

Section: A Wi-fimentioning

confidence: 99%

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Room-Level Localization System Based on LoRa Backscatters

2021

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The aim of this paper is to propose a novel room-level localization approach to locate LoRa backscatter devices, which can be easily embedded into wearable devices or smartphones. The advantages of this system lie in its series of low-cost, low-power, low-complexity and long-range features. LoRa backscattering operates by alternatively connecting an antenna, through a switch, to two loads with high reflection coefficients and opposite phase. The result is a frequency shift of the LoRa incident signal equal to the backscatter switching frequency. The localization system comprises several LoRa receivers distributed among the rooms, a LoRa transmitter located at a central point and the backscatter device, which is carried or worn by a subject. The position of the LoRa backscatter device can be determined by comparing the received signal strength between all receivers. In order to improve the accuracy of the system, different machine learning classifiers were compared. System performance was tested in a real-life scenario, achieving an accuracy up to 89.7% using linear discriminant analysis (LDA). INDEX TERMS Backscatter communications, LoRa, localization, wireless sensor networks, RFID, zeropower sensor, internet of things (IoT).

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Section: Discussionmentioning

confidence: 99%

Section: A Wi-fimentioning

confidence: 99%

Room-Level Localization System Based on LoRa Backscatters

2021

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“…In this paper, we do not consider opportunistic approaches as a separate class. For detailed information on (non-collaborative) IPSs, we refer to the excellent recent reviews and surveys available in literature, such as [ 53 , 70 , 71 ].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Indoor Positioning Systems: A Systematic Review

Pascacio

Casteleyn

Torres-Sospedra

et al. 2021

Sensors

101

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Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified.

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“…However, all these technologies have challenges with inaccuracy, privacy, environmental robustness, and system complexity [9]. For instance, the HD camera system and other technologies such as Wi-Fi, Bluetooth, and UWB are used for positioning [10][11][12]. In these studies, Machine Learning (ML) methods are employed to detect people.…”

Section: Introductionmentioning

confidence: 99%

Indoor Detection and Tracking of People Using mmWave Sensor

et al. 2021

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This paper proposes a new indoor people detection and tracking system using a millimeter-wave (mmWave) radar sensor. Firstly, a systematic approach for people detection and tracking is presented—a static clutter removal algorithm used for removing mmWave radar data’s static points. Two efficient clustering algorithms are used to cluster and identify people in a scene. The recursive Kalman filter tracking algorithm with data association is used to track multiple people simultaneously. Secondly, a fast indoor people detection and tracking system is designed based on our proposed algorithms. The method is lightweight enough for scalability and portability, and we can execute it in real time on a Raspberry Pi 4. Finally, the proposed method is validated by comparing it with the Texas Instruments (TI) system. The proposed system’s experimental accuracy ranged from 98% (calculated by misclassification errors) for one person to 65% for five people. The average position errors at positions 1, 2, and 3 are 0.2992 meters, 0.3271 meters, and 0.3171 meters, respectively. In comparison, the Texas Instruments system had an experimental accuracy ranging from 96% for one person to 45% for five people. The average position errors at positions 1, 2, and 3 are 0.3283 meters, 0.3116 meters, and 0.3343 meters, respectively. The proposed method’s advantage is demonstrated in terms of tracking accuracy, computation time, and scalability.

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A Survey of Machine Learning for Indoor Positioning

Cited by 171 publications

References 137 publications

Room-Level Localization System Based on LoRa Backscatters

Room-Level Localization System Based on LoRa Backscatters

Collaborative Indoor Positioning Systems: A Systematic Review

Indoor Detection and Tracking of People Using mmWave Sensor

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