An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model

Alam, Fakhrul; Chew, M. T.; Wenge, Tapiwanashe; Gupta, Gourab Sen

doi:10.1109/tim.2018.2870263

Cited by 93 publications

(80 citation statements)

References 34 publications

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“…There are several methods described in the literature [15,16] to derive the position of a mobile receiver using the information that is embedded in these light signals. Classical VLP schemes generally deploy lateration [17], angulation [17], or fingerprinting [18] algorithms to retrieve the coordinates of the receiver. Lateration requires estimations of the distance between the transmitters and receiver.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Raes

Knudde

Bruycker

et al. 2020

Sensors

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In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.

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Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Raes

Knudde

Bruycker

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The distance between T i and T j , d ij (i, j ∈ {1, 2, 3} , i j), is known in advance. Besides, w c i (i ∈ {1, 2, 3}), which can be calculated by (4), are the vectors from the receiver to T i in CCS. Furthermore, α ij (i, j ∈ {1, 2, 3} , i j) is the angle between w w i and w w j , i.e., α ij = ∠T i o c T j , which can be calculated as…”

Section: Incidence Angle Estimationmentioning

confidence: 99%

Camera Assisted Received Signal Strength Ratio Algorithm for Indoor Visible Light Positioning

et al. 2019

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In this paper, a received signal strength assisted Perspective-three-Point positioning algorithm (R-P3P) is proposed for visible light positioning (VLP) systems. The basic idea of R-P3P is to joint visual and strength information to estimate the receiver position using 3 LEDs regardless of the LEDs' orientations. R-P3P first utilizes visual information captured by the camera to estimate the incidence angles of visible lights. Then, R-P3P calculates the candidate distances between the LEDs and the receiver based on the law of cosines and Wu-Ritt's zero decomposition method. Based on the incidence angles, the candidate distances and the physical characteristics of the LEDs, R-P3P can select the exact distances from all the candidate distances. Finally, the linear least square (LLS) method is employed to estimate the position of the receiver. Due to the combination of visual and strength information of visible light signals, R-P3P can achieve high accuracy using 3 LEDs regardless of the LEDs' orientations. Simulation results show that R-P3P can achieve positioning accuracy within 10 cm over 70% indoor area with low complexity regardless of LEDs orientations.

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“…In [32], Alam et al evaluated a Visible Light Positioning system based on RSS. They generated an offline fingerprint database in a 3.3 × 2.1 m floor space illuminated by four luminaires, each one emitting a different square-wave frequency.…”

Section: Photodiode-based Visible Light Positioningmentioning

confidence: 99%

“…Lin et al [9] 5 cm @ 1.2 m OCC Very low Li et al [14] 6 cm @ 0.8 m OCC Very Low Hossar et al [15] 10 cm @ 3 m OCC Low Tanaka et al [42] 5 cm @ 1.95 m OCC Low Chen et al [22] >10 cm @ 1 m VLC Low Chen and Lain [23] 3 cm @ 1 m OCC Low Alam et al [32] 2.2 cm @ 2.4 m Fingerprint High Konings et al [33] 10 cm @ 2.4 m VLC + RF Fingerprint High Proposed scheme sub-cm @ 2.2 m OCC + TDoA Low Proposed scheme with mode sub-cm @ 2.7 m OCC + TDoA Low…”

Section: System Error Technique Complexitymentioning

confidence: 99%

A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival

Rabadán

Guerra

et al. 2019

Applied Sciences

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In this work, a new Time Difference of Arrival (TDoA) scheme for distance measurement based on Optical Camera Communication (OCC) systems is proposed. It relies on the use of optical pulses instead of radio-frequency signals as the time reference triggers, and the introduction of a rolling shutter camera, whose characteristics allows substituting the timer modules used in conventional TDoA techniques by image processing of the illuminated area in the picture. This processing on the camera’s images provides time measurements and implies and specific analysis, which is presented in this work. The system performance and properties, such as resolution and range, mainly depends on the camera characteristics, such as the frames capture rate and the image quality. This new technique is suitable to be implemented in smartphones or other Commercial Off-The-Shelf (COTS) devices equipped with a camera and speakers.

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An Accurate Visible Light Positioning System Using Regenerated Fingerprint Database Based on Calibrated Propagation Model

Cited by 93 publications

References 34 publications

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Camera Assisted Received Signal Strength Ratio Algorithm for Indoor Visible Light Positioning

A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival

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