Performance improvement of indoor positioning using light-emitting diodes and an image sensor for light-emitting diode communication

Hossen, Md. Sazzad; Park, Yong Il; Kim, Ki-Doo

doi:10.1117/1.oe.54.3.035108

Cited by 14 publications

(12 citation statements)

References 7 publications

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“…Moreover, compared with other works, the proposed algorithm in this paper has a good real-time performance. References [23,24,25] can provide different degrees of positioning accuracy, which can reach decimeter-level, within ten centimeters, or even millimeter-level. However, these works focus on static positioning only and real-time ability is not considered.…”

Section: Experimental Setup and Results Analysismentioning

confidence: 99%

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Huang

Guan

Fan

et al. 2018

Sensors

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An improved Cam-Shift algorithm with a Kalman filter applied to image-sensor based on outdoor visible light communication (OVLC) is presented in this paper. The proposed optimized tracking algorithm is used to track and extract the region of the target signal source Light Emitting Diode (LED) that carries modulated information for data transmission. Extracting the target signal source LED area is the premise of an image-sensor-based VLC system, especially in outdoor dynamic scenes. However, most of the existing VLC studies focus on data transmission rate, visible light positioning, etc. While the actual first step of realizing communication is usually ignored in the field of VLC, especially when the transmitter (signal source LED) or the receiver (image sensor) is moving in a more complex outdoor environment. Therefore, an improved tracking algorithm is proposed in this paper, aiming at solving the problem of extracting the region of the target signal source LED accurately in dynamic scenes with different interferences so as to promote the feasibility of VLC applications in outdoor scenes. The proposed algorithm considers color characteristics and special distribution characteristics of the moving target at the same time. The image is converted to a color probability distribution map based on the color histogram of the target and adaptively adjusts the location and size of the search window based on the results obtained from the previous frame. Meanwhile, it predicts the motion state of the target in the next frame according to the position and velocity information of the current frame to enhance accuracy and robustness of tracking. Experimental results show that the tracking error of the proposed algorithm is 0.85 cm and the computational time of processing one frame is 0.042 s. Besides, results also show that the improved algorithm can track and extract the target signal source LED area completely and accurately in an environment of many interference factors. This study confirms that the proposed algorithm can be applied to an OVLC system with many interferences to realize the actual first step of communication in an image-sensor-based VLC system, laying foundations for subsequent data transmission and other steps.

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Section: Experimental Setup and Results Analysismentioning

confidence: 99%

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Huang

Guan

Fan

et al. 2018

Sensors

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show abstract

“…Moreover, compared with other works, the algorithm proposed in this paper also has a good real-time performance. References [26]- [28] can provide different degrees of positioning accuracy, which can reach decimeter-level, within ten centimeters, or even millimeter-level. However, these works only focus on the accuracy of positioning and ignore real-time performance.…”

Section: Real-time Performancementioning

confidence: 99%

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using an Improved Particle Filter Algorithm Based on Cam-Shift Algorithm

2019

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An improved particle filter algorithm based on Cam-Shift algorithm applied to outdoor visible light communication (outdoor-VLC) is presented in this paper. In the outdoor-VLC system, accurately and completely tracking and extracting the target signal source Light Emitting Diode (LED) area is the premise for realizing communication. However, few existing studies pay attention to it. In the dynamic outdoor environment, there will be a lot of different environmental interferences (such as background interference, similar object interference, etc.), which greatly increases the difficulty of tracking and extracting the target signal source LED area. Therefore, in this paper, an improved tracking algorithm is proposed to solve the problem of how to track and extract the target signal source LED area accurately, stably and in real time in the outdoor-VLC system under various environmental interferences, so as to increase the feasibility of practical application of VLC system in outdoor scenes. This improved algorithm combines the particle filter algorithm and Cam-Shift algorithm originally. Experimental results show that the proposed algorithm has good accuracy, robustness and real-time performance under the environment of multiple interference factors. Accordingly, the proposed algorithm can be applied to the outdoor-VLC system with various environmental interferences, and can realize the actual first step of communication in VLC system based on image sensor well, laying a foundation for feature extraction, data transmission and other subsequent steps.

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“…For the above reasons, more and more researchers are focusing on how to improve the performance of indoor visible light positioning (VLP) systems. Both photo-diodes (PDs) and image sensors can be utilized as the detector [5,6,7,8,9,10,11]. Taking into consideration the system complexity and cost, PD-based systems using the RSS algorithm are most widely reported [5,6,7,8].…”

Section: Introductionmentioning

confidence: 99%

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

Zhang

Cui

Feng

et al. 2019

Sensors

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In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

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Performance improvement of indoor positioning using light-emitting diodes and an image sensor for light-emitting diode communication

Cited by 14 publications

References 7 publications

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using a Cam-Shift Algorithm and Kalman Filter

Improved Target Signal Source Tracking and Extraction Method Based on Outdoor Visible Light Communication Using an Improved Particle Filter Algorithm Based on Cam-Shift Algorithm

High-Precision Indoor Visible Light Positioning Using Modified Momentum Back Propagation Neural Network with Sparse Training Point

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