Precise Indoor Positioning Based on Acoustic Ranging in Smartphone

Chen, Ruizhi; Li, Zheng; Yu, Feng; Guo, Guangyi; Xu, Shihao; Qian, Long; Liu, Zuoya; Huang, Lixiong

doi:10.1109/tim.2021.3082269

Cited by 39 publications

(12 citation statements)

References 25 publications

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“…The experimental results showed a significant improvement over the homogeneous results, achieving a convergence error range up to a few distances from the receiver. BLE signals are a promising solution because they are inexpensive, easy to deploy, and have low power requirements [35].…”

Section: Discussionmentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.

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Section: Discussionmentioning

confidence: 99%

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Maaloul¹,

Lejdel²,

Clementini

et al. 2023

Bulletin EEI

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“…Chen et al [218] present a system using acoustic ranging signals, which can be used in almost all commercial off-the-shelf (COTS) smartphones. A two-stage algorithm based on a generalized cross-correlation (GCC) approach and time-frequency-based feature extraction is designed to detect the modulated signal transmitted by each specially designed anchor station consisting of a loudspeaker.…”

Section: Comparison Of Different Indoor Positioning Techniquesmentioning

confidence: 99%

A Survey of Magnetic-Field-Based Indoor Localization

Ouyang

Abed-Meraim

2022

Electronics

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Magnetic fields have attracted considerable attention in indoor localization due to their ubiquitous and infrastructure-free characteristics. This survey provides a comprehensive review of magnetic-field-based indoor localization methods. We first introduce characteristics of the magnetic field, its advantages, and its challenges. We then describe the magnetometer model and the effect of ferromagnetic interference. We also present coordinate systems commonly used for magnetic field localization and describe their transformation relationships. We then compare the existing publicly available magnetic field benchmark datasets, present magnetometer calibration algorithms, and show how efficiently magnetic field maps can be built. We also summarize state-of-the-art magnetic field localization methods (e.g., magnetic landmarks, dynamic time warping, magnetic fingerprinting, filters, simultaneous localization and mapping, and neural network). The smartphone-based pedestrian dead reckoning approach is also reviewed.

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“…To realize robust ranging, Refs. [ 40 , 41 ] utilize the Chirp signal, but the NLOS effect is not considered in the algorithm experiment. In [ 42 ], fractional Fourier transform is applied to avoid the multipath effect, but the complexity of it is overwhelmed.…”

Section: Related Workmentioning

confidence: 99%

“…In order to further verify the applicability of the proposed ranging method, the three cases of the experiment are carried out. The ranging result will be compared with the traditional method, which only uses maximum cross-correlation (named as Peak) [ 41 , 48 , 55 , 56 , 57 ], two-stage search [ 40 ] (only use two-stage search) and env-two-stage proposed. The two smartphones are Huawei Mate40 Pro and Huawei P20Pro.…”

Section: Experiments and Simulationmentioning

confidence: 99%

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal

Jia

Shu

Wang

et al. 2022

Sensors

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With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz–23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

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Precise Indoor Positioning Based on Acoustic Ranging in Smartphone

Cited by 39 publications

References 25 publications

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

Bluetooth beacons based indoor positioning in a shopping malls using machine learning

A Survey of Magnetic-Field-Based Indoor Localization

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal

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