Bluetooth Indoor Positioning Based on RSSI and Kalman Filter

Zhang, Cheng; Yuan, Jiazheng; Liu, Hongzhe; Qiu, Jing

doi:10.1007/s11277-017-4371-4

Cited by 120 publications

(60 citation statements)

References 21 publications

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“…In [26], the authors set up the CellinDeep system-based Wi-Fi signal, which used data augmenter techniques and mean filtering to address practical challenges caused by signal noise. Zhou et al [31] used Kalman filtering to reduce drift and oscillation on a received signal strength indicator (RSSI) caused by the noise overlap-add in Bluetooth locations. Nevertheless, most of these methods rely on multiple sensors, and there has been relatively little research on a single Wi-Fi constraint location.…”

Section: Related Workmentioning

confidence: 99%

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Wang

Gao

et al. 2020

Applied Sciences

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Currently, indoor locations based on the received signal strength (RSS) of Wi-Fi are attracting more and more attention thanks to the technology’s low cost, low power consumption and wide availability in mobile devices. However, the accuracy of Wi-Fi positioning is limited, due to the signal fluctuation and indoor multipath interference. In order to overcome this problem, this paper proposes a robust and accurate Wi-Fi fingerprint location recognition method based on a deep neural network (DNN). A stacked denoising auto-encoder (SDAE) is used to extract robust features from noisy RSS to construct a feature-weighted fingerprint database offline. We use the combination of the weights of posteriori probability and geometric relationship of fingerprint points to calculate the coordinates of unknown points online. In addition, we use constrained Kalman filtering and hidden Markov models (HMM) to smooth and optimize positioning results and overcome the influence of gross error on positioning results, combined with characteristics of user movement in buildings, both dynamic and static. The experiment shows that the DNN is feasible for position recognition, and the method proposed in this paper is more accurate and stable than the commonly used Wi-Fi positioning methods in different scenes.

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Section: Related Workmentioning

confidence: 99%

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Wang

Gao

et al. 2020

Applied Sciences

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“…Regarding generated or artificial sources of data, few approaches have been proposed to develop ILS. For instance, there are several proposals that use the propagation of signals, such as Bluetooth, Zigbee, WiFi and radio frequency identification (RFID), among others [5][6][7][8]. These proposals have proved their ability to develop accurate and robust ILS, through the use of sensors and diverse devices, allowing the development of commercial and well-known systems, e.g., LANDMARC [9], Bluepos [10], CLIPS [11], to mention some [12,13].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Galván-Tejada

Zanella-Calzada

García-Domínguez

et al. 2020

IJGI

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Estimation of indoor location represents an interesting research topic since it is a main contextual variable for location bases services (LBS), eHealth applications and commercial systems, among others. For instance, hospitals require location data of their employees, as well as the location of their patients to offer services based on these locations at the correct moments of their needs. Several approaches have been proposed to tackle this problem using different types of artificial or natural signals (i.e., wifi, bluetooth, rfid, sound, movement,etc).In this work, it is proposed the development of an indoor location estimator system, relying in the data provided by the magnetic field of the rooms, which has been demonstrated that is unique and quasi-stationary. For this purpose, it is analyzed the spectral evolution of the magnetic field data viewed as a bidimensional heatmap, avoiding temporal dependencies. A Fourier transform is applied to the bidimensional heatmap of the magnetic field data to feed a convolutional neural network (CNN) to generate a model to estimate the user's location in a building. The evaluation of the CNN model to deploy an indoor location system (ILS) is done through measuring the Receiver Operating Characteristic (ROC) curve to observe the behavior in terms of sensitivity and specificity. Our experiments achieve a 0.99 Area Under the Curve (AUC) in the training data-set and a 0.74 in a total blind data set.

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“…The other location technology is to make use of pre-deployed additional base stations (BS) to achieve precise indoor positionings, such as radio frequency identification (RFID), 3,4 ultra-wideband (UWB), [5][6][7][8][9] Li-Fi, 10 ultrasound, [11][12][13][14][15][16][17][18] Bluetooth, 19,20 and ZigBee 21 technology. This type of technology can be used to increase accuracy by increasing the number of reference nodes, as shown in Figure 1; the reference nodes are arranged indoor and the positioning accuracy can be centimeter level.…”

Section: Introductionmentioning

confidence: 99%

“…Bluetooth for indoor location is also relatively mature, 20 and positioning accuracy is higher than Wi-Fi, equivalent to UWB. The main advantage of Bluetooth is that it is easy to integrate into smartphones and other mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic localization method based on receiver array optimization schemes

Luo

Hu-cheng

Yan

et al. 2018

International Journal of Distributed Sensor Networks

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Ultrasonic localization system using trilateration has been adopted in various types of full-enclosed and semi-enclosed space. For small-scale or enclosed scenarios, there is an important phenomenon, that is, the ultrasonic wave may rapidly reflect back and forth between the walls to form different normal waves during the process of propagation, which may break the inverse square law of sound intensity (the sound intensity in free space is inversely proportional to the square of the distance). The other problem is that the sound intensity of some local corner points are obviously stronger than those points near the acoustic source, which may confuse the ultrasonic receivers and bring significant differences in measurement errors. To avoid excessive errors influence for the positioning accuracy, in this article, a novel ultrasonic localization method based on receiver array optimization schemes is reported. The main purpose is to activate the ultrasonic receiver in the area as well as shield the ultrasonic receiver outside the area. The system error will be reduced based on this method, and most of the redundant data will be eliminated. The simulation results are visualized and compared with the existing methods. The positioning accuracy measured by our method is increased significantly.

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Bluetooth Indoor Positioning Based on RSSI and Kalman Filter

Cited by 120 publications

References 21 publications

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Estimation of Indoor Location Through Magnetic Field Data: An Approach Based On Convolutional Neural Networks

Ultrasonic localization method based on receiver array optimization schemes

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