Analysis of positioning accuracy corresponding to the number of BLE beacons in indoor positioning system

Mobile Robot Indoor Positioning System has wide application in the industry and home automation field. Unfortunately, existing mobile robot indoor positioning methods often suffer from poor positioning accuracy, system instability, and need for extra installation efforts. In this paper, we propose a novel positioning system which applies the centralized positioning method into the mobile robot, in which real-time positioning is achieved via interactions between ARM and computer. We apply the Kernel extreme learning machine (K-ELM) algorithm as our positioning algorithm after comparing four different algorithms in simulation experiments. Real-world indoor localization experiments are conducted, and the results demonstrate that the proposed system can not only improve positioning accuracy but also greatly reduce the installation efforts since our system solely relies on Wi-Fi devices.

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Optical Boundaries for LED-Based Indoor Positioning System

Computation ◽

10.3390/computation7010007 ◽

2019 ◽

Vol 7 (1) ◽

pp. 7 ◽

Cited By ~ 1

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.

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An integrity monitoring algorithm for WiFi/PDR/smartphone-integrated indoor positioning system based on unscented Kalman filter

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01809-y ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Haiyun Yao ◽

Hong Shu ◽

Hongxing Sun ◽

B. G. Mousa ◽

Zhenghang Jiao ◽

...

Keyword(s):

Kalman Filter ◽

Computational Complexity ◽

Unscented Kalman Filter ◽

Indoor Positioning ◽

Positioning System ◽

Positioning Accuracy ◽

Integrity Monitoring ◽

Observation Vector ◽

Indoor Positioning System ◽

Monitoring Algorithm

AbstractIndoor positioning navigation technologies have developed rapidly, but little effort has been expended on integrity monitoring in Pedestrian Dead Reckoning (PDR) and WiFi indoor positioning navigation systems. PDR accuracy will drift over time. Meanwhile, WiFi positioning accuracy decreases in complex indoor environments due to severe multipath propagation and interference with signals when people move about. In our research, we aimed to improve positioning quality with an integrity monitoring algorithm for a WiFi/PDR-integrated indoor positioning system based on the unscented Kalman filter (UKF). The integrity monitoring is divided into three phases. A test statistic based on the innovation of UKF determines whether the positioning system is abnormal. Once a positioning system abnormality is detected, a robust UKF (RUKF) is triggered to achieve higher positioning accuracy. Again, the innovation of RUKF is used to judge the outliers in observations and identify positioning system faults. In the last integrity monitoring phase, users will be alerted in time to reduce the risk from positioning fault. We conducted a simulation to analyze the computational complexity of integrity monitoring. The results showed that it did not substantially increase the overall computational complexity when the number of dimensions in the state vector and observation vector in the system is small (< 20). In practice, the number of dimensions of state vector and observation vector in an indoor positioning system rarely exceeds 20. The proposed integrity monitoring algorithm was tested in two field experiments, showing that the proposed algorithm is quite robust, yielding higher positioning accuracy than the traditional method, using only UKF.

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

Mathematical Problems in Engineering ◽

10.1155/2020/4696198 ◽

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.

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

International Journal of Energy Optimization and Engineering ◽

10.4018/ijeoe.2019040103 ◽

2019 ◽

Vol 8 (2) ◽

pp. 47-60 ◽

Cited By ~ 1

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.

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