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.

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 Optical Boundaries for LED-Based Indoor Positioning System

Computation ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 7 ◽  
Author(s):  
Olaoluwa Popoola ◽  
Sinan Sinanović ◽  
Wasiu Popoola ◽  
Roberto Ramirez-Iniguez
Keyword(s):  
Mathematical Model ◽  
Indoor Positioning ◽  
Experimental Measurements ◽  
Positioning System ◽  
Positioning Error ◽  
Positioning Accuracy ◽  
Light Emitting ◽  
Positioning Systems ◽  
Indoor Positioning System ◽  
Positioning Time

Overlap of footprints of light emitting diodes (LEDs) increases the positioning accuracy of wearable LED indoor positioning systems (IPS) but such an approach assumes that the footprint boundaries are defined. In this work, we develop a mathematical model for defining the footprint boundaries of an LED in terms of a threshold angle instead of the conventional half or full angle. To show the effect of the threshold angle, we compare how overlaps and receiver tilts affect the performance of an LED-based IPS when the optical boundary is defined at the threshold angle and at the full angle. Using experimental measurements, simulations, and theoretical analysis, the effect of the defined threshold angle is estimated. The results show that the positional time when using the newly defined threshold angle is 12 times shorter than the time when the full angle is used. When the effect of tilt is considered, the threshold angle time is 22 times shorter than the full angle positioning time. Regarding accuracy, it is shown in this work that a positioning error as low as 230 mm can be obtained. Consequently, while the IPS gives a very low positioning error, a defined threshold angle reduces delays in an overlap-based LED IPS.

A Multi-Criteria Decision Strategy to Select a Machine Learning Method for Indoor Positioning System

2018 ◽  
Vol 27 (05) ◽  
pp. 1850018 ◽  
Author(s):  
Ahmet Yazıcı ◽  
Sinem Bozkurt Keser ◽  
Serkan Günal ◽  
Uğur Yayan
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Learning Algorithms ◽  
Computation Time ◽  
Indoor Positioning ◽  
Support Vector ◽  
Positioning System ◽  
Decision Strategy ◽  
Positioning Systems ◽  
Indoor Positioning System

Indoor positioning system is an active research area. There are various performance metrics such as accuracy, computation time, precision, and f-score in machine learning based indoor positioning systems. The aim of this study is to present a multi-criteria decision strategy to determine suitable machine learning methods for a specific indoor positioning system. This helps to evaluate the performance of machine learning algorithms considering multiple criteria. During the experiments, UJIIndoorLoc, KIOS and RFKON datasets are used from the positioning literature. The algorithms such as k-nearest neighbor, support vector machine, decision tree, naïve bayes and bayesian networks are compared using these datasets. In addition to these, ensemble learning algorithms, namely adaboost and bagging, are utilized to improve the performance of these classifiers. As a conclusion, the test results for any specific dataset are reevaluated using the performance metrics such as accuracy, f-score and computation time, and a multi-criteria decision strategy is proposed to find the most convenient algorithm. The analytical hierarchy process is used for multi-criteria decision. To the best of our knowledge, this is the first work to select the proper machine learning algorithm for an indoor positioning system using multi-criteria decision strategy.

scholarly journals A Low-Cost Visible Light Positioning System for Indoor Positioning

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5145 ◽  
Author(s):  
Juan Torres ◽  
Aitor Montes ◽  
Sandra Mendoza ◽  
Pedro Fernández ◽  
Juan Betancourt ◽  
...  
Keyword(s):  
Visible Light ◽  
Electromagnetic Interference ◽  
Urban Areas ◽  
Low Cost ◽  
Visible Light Communication ◽  
Indoor Positioning ◽  
Positioning System ◽  
Time To Market ◽  
Position Information ◽  
Positioning Systems

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.

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 Analyzing Machine Learning Models with Gaussian Process for the Indoor Positioning System

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yunxin Xie ◽  
Chenyang Zhu ◽  
Wei Jiang ◽  
Jia Bi ◽  
Zhengwei Zhu
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Positioning System ◽  
Learning Approaches ◽  
Positioning Accuracy ◽  
Access Points ◽  
Indoor Positioning System

Recently, there has been growing interest in improving the efficiency and accuracy of the Indoor Positioning System (IPS). The Received Signal Strength- (RSS-) based fingerprinting technique is essential for indoor localization. However, it is challenging to estimate the indoor position based on RSS’s measurement under the complex indoor environment. This paper evaluates three machine learning approaches and Gaussian Process (GP) regression with three different kernels to get the best indoor positioning model. The hyperparameter tuning technique is used to select the optimum parameter set for each model. Experiments are carried out with RSS data from seven access points (AP). Results show that GP with a rational quadratic kernel and eXtreme gradient tree boosting model has the best positioning accuracy compared to other models. In contrast, the eXtreme gradient tree boosting model could achieve higher positioning accuracy with smaller training size and fewer access points.

Visible Light Communication System for Indoor Positioning Using Solar Cell as Receiver

2019 ◽  
Vol 8 (2) ◽  
pp. 47-60 ◽  
Author(s):  
Ameur Chaabna ◽  
Abdesselam Babouri ◽  
Chuanxi Huang ◽  
Xun Zhang
Keyword(s):  
Solar Cell ◽  
Visible Light ◽  
Visible Light Communication ◽  
Indoor Positioning ◽  
Positioning System ◽  
Optical Receivers ◽  
Positioning Accuracy ◽  
Current Voltage ◽  
Indoor Positioning System ◽  
Average Position

This article studies an indoor positioning system (IPS) based on visible light communication (VLC) using the solar cells as optical receivers. Due to the solar cell characteristics, the proposed system offers simultaneously communication and energy gathering. The current - voltage (I-V) and power - resistor (P-R) curves of solar cell under visible light is studied. The frequency response is investigated. The effect of solar irradiance interference on the VLC-based indoor positioning system (VLC-IPS) efficiency is also shown. A trilateration technique is used for estimating receiver's location. The obtained results show that the proposed system can simultaneously receive data and collect energy and it provides a suitable positioning accuracy with an average position error less than 3cm. The studied system can be considered an eco-friendly and promising technology.

scholarly journals High-Precision RTT-Based Indoor Positioning System Using RCDN and RPN

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3701
Author(s):  
Ju-Hyeon Seong ◽  
Soo-Hwan Lee ◽  
Won-Yeol Kim ◽  
Dong-Hoan Seo
Keyword(s):  
High Precision ◽  
Distance Estimation ◽  
Sampling Period ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Positioning System ◽  
Positioning Systems ◽  
Positioning Errors ◽  
Location Determination ◽  
Indoor Positioning System

Wi-Fi round-trip timing (RTT) was applied to indoor positioning systems based on distance estimation. RTT has a higher reception instability than the received signal strength indicator (RSSI)-based fingerprint in non-line-of-sight (NLOS) environments with many obstacles, resulting in large positioning errors due to multipath fading. To solve these problems, in this paper, we propose high-precision RTT-based indoor positioning system using an RTT compensation distance network (RCDN) and a region proposal network (RPN). The proposed method consists of a CNN-based RCDN for improving the prediction accuracy and learning rate of the received distances and a recurrent neural network-based RPN for real-time positioning, implemented in an end-to-end manner. The proposed RCDN collects and corrects a stable and reliable distance prediction value from each RTT transmitter by applying a scanning step to increase the reception rate of the TOF-based RTT with unstable reception. In addition, the user location is derived using the fingerprint-based location determination method through the RPN in which division processing is applied to the distances of the RTT corrected in the RCDN using the characteristics of the fast-sampling period.

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