scholarly journals High Precision Indoor Visible Light Positioning Algorithm Based on Double LEDs Using CMOS Image Sensor

2019 ◽  
Vol 9 (6) ◽  
pp. 1238 ◽  
Author(s):  
Weipeng Guan ◽  
Xinjie Zhang ◽  
Yuxiang Wu ◽  
Zekun Xie ◽  
Jingyi Li ◽  
...  
Keyword(s):  
Visible Light ◽  
Real Time ◽  
Computing Time ◽  
Computational Cost ◽  
Cmos Image Sensor ◽  
Indoor Positioning ◽  
Image Sensor ◽  
Positioning System ◽  
Satisfactory Performance ◽  
Positioning Algorithm

Visible Light Positioning (VLP) is widely recognized as a cost-effective solution for indoor positioning with increasing demand. However, the nonlinearity and highly complex relationship between three-dimensional world coordinate and two-dimensional image coordinate hinders the good performance of image-sensor-based VLP. Therefore, there is a need to develop effective VLP algorithms to locate the positioning terminal using image sensor. Besides, due to the high computational cost of image processing, most existing VLP systems do not achieve satisfactory performance in terms of real-time ability and positioning accuracy, both of which are significant for the performance of indoor positioning system. In addition, the accurate identification of the ID information of each LED (LED-ID) is important for positioning, because if the LED-ID is not recognized well, the positioning can only be achieved in a particular positioning unit and cannot be applied to a large scene with many LEDs. Therefore, an effective image-sensor-based double-light positioning system is proposed in this paper to solve the above problems. We also set up relevant experiments to test the performance of the proposed system, which utilizes the rolling shutter mechanism of the Complementary Metal Oxide Semiconductor (CMOS) image sensor. Machine learning was used to identify the LED-ID for better results. Simulation results show that the proposed double-light positioning system could deliver satisfactory performance in terms of both the real-time ability and the accuracy of positioning. Moreover, the proposed double-light positioning algorithm has low complexity and takes the symmetry problem of angle into consideration, which has never been considered before. Experiments confirmed that the proposed double-light positioning system can provide an accuracy of 3.85 cm with an average computing time of 56.28 ms, making it a promising candidate for future indoor positioning applications.

scholarly journals Multilateration Approach for Wide Range Visible Light Indoor Positioning System Using Mobile CMOS Image Sensor

2021 ◽  
Vol 11 (16) ◽  
pp. 7308
Author(s):  
Md Habibur Rahman ◽  
Mohammad Abrar Shakil Sejan ◽  
Wan-Young Chung
Keyword(s):  
Visible Light ◽  
Real Time ◽  
Indoor Positioning ◽  
Image Sensor ◽  
Computational Time ◽  
Positioning System ◽  
Horizontal Distance ◽  
Large Area ◽  
Positioning Systems ◽  
Wide Range

Visible light positioning (VLP) is a cost-effective solution to the increasing demand for real-time indoor positioning. However, owing to high computational costs and complicated image processing procedures, most of the existing VLP systems fail to deliver real-time positioning ability and better accuracy for image sensor-based large-area indoor environments. In this study, an effective method is proposed to receive coordinate information from multiple light-emitting diode (LED) lights simultaneously. It provides better accuracy in large experimental areas with many LEDs by using a smartphone-embedded image sensor as a terminal device and the existing LED lighting infrastructure. A flicker-free frequency shift on–off keying line coding modulation scheme was designed for the positioning system to ensure a constant modulated frequency. We tested the performance of the decoding accuracy with respect to vertical and horizontal distance, which utilizes a rolling shutter mechanism of a complementary metal-oxide-semiconductor image sensor. The experimental results of the proposed positioning system can provide centimeter-level accuracy with low computational time, rendering it a promising solution for the future direction of large-area indoor positioning systems.

scholarly journals Reduced Tilting Effect of Smartphone CMOS Image Sensor in Visible Light Indoor Positioning

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1635
Author(s):  
Md Habibur Rahman ◽  
Mohammad Abrar Shakil Sejan ◽  
Jong-Jin Kim ◽  
Wan-Young Chung
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Light Emitting Diode ◽  
Cmos Image Sensor ◽  
Indoor Positioning ◽  
Image Sensor ◽  
Image Features ◽  
Oxide Semiconductor ◽  
Positioning System ◽  
Indoor Positioning System

Visible light positioning (VLP) using complementary metal–oxide–semiconductor (CMOS) image sensors is a cost-effective solution to the increasing demand for an indoor positioning system. However, in most of the existing VLP systems with an image sensor, researchers assume that the receiving image sensor is positioned parallel to the indoor floor without any tilting and, thus, have only focused on the high-precision positioning algorithm and ignored the proper light-emitting diode (LED)-ID recognition. To address these limitations, we present, herein, a smartphone CMOS image sensor and visible light-based indoor localization system for a receiver device in a tilted position, and we have applied a machine learning approach for optimized LED-ID detection. For detection of the LED-ID, we generated different features for different LED-IDs and utilize a machine learning method to identify each ID as opposed to using the conventional coding and decoding method. An image processing method was used for the image features extraction and selection. We utilized the rolling shutter mechanism of the smartphone CMOS image sensor in our indoor positioning system. Additionally, to improve the LED-ID detection and positioning accuracy with the tilting of the receiver, we utilized the embedded fusion sensors of the smartphone (e.g., accelerometer, gyroscope, and magnetometer, which can be used to extract the yaw, pitch, and roll angles). The experimental results for the proposed positioning system show that it can provide 2.49, 4.63, 8.46, and 12.20 cm accuracy with angles of 0, 5, 10, and 15°, respectively, within a 2 m × 2 m × 2 m positioning area.

scholarly journals Design of a Real-Time Indoor Positioning System Based on Visible Light Communication

2020 ◽  
Vol 29 (3) ◽  
pp. 445-451
Author(s):  
M. S. P. dos S. Lima Junior ◽  
M. P. Halapi ◽  
E. Udvary
Keyword(s):  
Visible Light ◽  
Real Time ◽  
Visible Light Communication ◽  
Indoor Positioning ◽  
Positioning System ◽  
Indoor Positioning System

Visible light positioning system based on CMOS image sensor using particle filter tracking and detecting algorithm

2019 ◽  
Vol 444 ◽  
pp. 9-20 ◽  
Author(s):  
Yuxiang Wu ◽  
Weipeng Guan ◽  
Xinjie Zhang ◽  
Mouxiao Huang ◽  
Junyi Cao
Keyword(s):  
Visible Light ◽  
Particle Filter ◽  
Cmos Image Sensor ◽  
Image Sensor ◽  
Positioning System

scholarly journals Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

2019 ◽  
Vol 9 (6) ◽  
pp. 1048 ◽  
Author(s):  
Huy Tran ◽  
Cheolkeun Ha
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Noise Reduction ◽  
Indoor Positioning ◽  
Computational Time ◽  
Dual Function ◽  
Positioning System ◽  
Positioning Accuracy ◽  
Positioning Systems ◽  
Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

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