Three-dimensional indoor light positioning algorithm based on nonlinear estimation

Gu, Wenbin; Kavehrad, M.; Aminikashani, Mohammadreza

doi:10.1117/12.2209240

Cited by 7 publications

(3 citation statements)

References 9 publications

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“…One popular way to evolve from a 2D VLP algorithm to a 3D one is to conduct a brute-force search on several parallel 2D layers at various heights. After obtaining the horizontal positions on all candidate layers at a pre-defined height set, the estimated 3D position is determined as the one that most likely fulfills the constraint among the coordinates of the three dimensions [ 16 , 21 , 22 ]. To improve the efficiency in height estimation, a fast search method based on the golden section search (GSS) algorithm has been proposed which can significantly reduce the running time [ 16 ].…”

Section: Introductionmentioning

confidence: 62%

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

Zhang

Chen

Zeng

et al. 2020

Sensors

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To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.

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Section: Introductionmentioning

confidence: 62%

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

Zhang

Chen

Zeng

et al. 2020

Sensors

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“…[13][14][15] In the previous methods, visible light, 13 WiFi, 14 and RFID 15 are used to measure the target position. The scheme in Xu et al 15 exploits the trilateral positioning algorithm and it is close to the proposed methods among the previous 3-D positioning methods.…”

Section: Comparison Of the Proposed And The Conventional Methodsmentioning

confidence: 99%

Three-dimensional positioning system using Bluetooth low-energy beacons

Park

Noh

Cho

2016

International Journal of Distributed Sensor Networks

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Bluetooth low-energy-based beacons have two different roles: ''broadcasting'' (by beacon device) and ''receiving'' (by sensing devices). It allows devices to receive information over a short distance. Thus, many researchers are actively investigating positioning methods that use beacons. However, the distance value of positioning in the existing methods is not always accurate. This article proposes a novel three-dimensional positioning system using Bluetooth low-energybased beacons. While the conventional methods measure distances by obtaining two-dimensional coordinates (x, y) through the received signal strength indicator value, the proposed method improves the measurement accuracy by obtaining three-dimensional coordinate values (x, y, z). Finally, while comparing the performance of this new method with that of existing methods, the proposed three-dimensional positioning system reduces the positioning error rate by 27.3% compared to the existing two-dimensional triangulation method.

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“…In prior works, the height of the receiver is known so that the coordinates on the horizontal plane can be calculated [ 13 ]. In this paper, assuming the height is unknown, we propose a reversed 3D VLC IPS using a single transmitter and two annular receivers with four PINs respectively.…”

Section: Introductionmentioning

confidence: 99%

Reversed Three-Dimensional Visible Light Indoor Positioning Utilizing Annular Receivers with Multi-Photodiodes

Zhao

Shi

et al. 2016

Sensors

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Exploiting the increasingly wide use of light emitting diodes (LEDs) lighting, in this paper we propose a reversed indoor positioning system (IPS) based on LED visible light communication (VLC) in order to improve indoor positioning accuracy. Unlike other VLC positioning systems, we employ two annular receivers with multi-photodiodes installed on the ceiling to locate the persons who carry LEDs. The basic idea is using multi-photodiodes to calculate the angle while using the received signal strength (RSS) method to calculate the distance. The experiment results show that the effective positioning range of the proposed system is 1.8 m when the distance between two receivers is 1.2 m. Moreover, a positioning error less than 0.2 m can be achieved under the condition that the radius of the PIN circle is between 0.16 m and 0.2 m, and the distance of the transmitter-receiver plane is less than 1.8 m, which will be effective in practice.

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Three-dimensional indoor light positioning algorithm based on nonlinear estimation

Cited by 7 publications

References 9 publications

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning

Three-dimensional positioning system using Bluetooth low-energy beacons

Reversed Three-Dimensional Visible Light Indoor Positioning Utilizing Annular Receivers with Multi-Photodiodes

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