Active RFID Indoor Location System Based on RSSI Ranging Correction

2013 ◽  
Vol 677 ◽  
pp. 449-454 ◽  
Author(s):  
Qing Bin Meng ◽  
Lan Ju ◽  
Jie Jin ◽  
Wei Xiang Li
Keyword(s):  
High Precision ◽  
Environmental Parameters ◽  
Statistical Calculation ◽  
Average Deviation ◽  
Active Rfid ◽  
Indoor Location ◽  
Location System ◽  
Statistical Experiments ◽  
Location Algorithm

For indoor location, an active RFID indoor location system is designed. The system is designed and implemented by using a RSSI-based ranging technology location algorithm. In this article, the author amended the RSSI ranging equation, proposed and implemented a way to extract and estimate the environmental parameters of specific application scenarios, and improved the accuracy of the RSSI ranging. After many statistical experiments, the results show that the system’s deviation can reach 10cm on the diagonal of the region, and about 30cm on the edge. Through statistical calculation, the average deviation of system position is about 10.6cm, which is a good location system of high precision. When implemented for different applications, this system has the advantages of simplicity and adaptability.

Transmission of Signals Using White LEDs for VLC Application

MRS Advances ◽  
2016 ◽  
Vol 1 (55) ◽  
pp. 3661-3666 ◽  
Author(s):  
P. Louro ◽  
V. Silva ◽  
J. Costa ◽  
M. A. Vieira ◽  
M. Vieira
Keyword(s):  
Optical Filter ◽  
Visible Light Communication ◽  
Indoor Navigation ◽  
Optical Signals ◽  
White Leds ◽  
Indoor Location ◽  
Optical Channels ◽  
Wavelength Sensitivity ◽  
Location Algorithm ◽  
The Individual

ABSTRCTIn this paper a photodetector working as an active optical filter device is used to detect modulated visible optical signals for applications based on Visible Light Communication (VLC). The proposed application demonstrates the viability of indoor positioning using VLC technology established by the modulation of indoor ultra-bright RGB white LEDs. The signals of the internal red and blue chips of the white LEDs were modulated at specific frequencies and the generated photocurrent was measured by a pin-pin photodetector based on a-SiC:H/a-Si:H. This device operates as a visible optical filter with controlled wavelength sensitivity through the use of adequate optical biasing light. Thus it is able to detect different wavelengths which allow the detection of the individual components of the tri-chromatic white LED. This possibility is the basis for the indoor location algorithm. We demonstrate the possibility of decoding four transmission optical channels supplied by two different wavelengths of white LEDs modulated under different bit sequences. The identification of the signals received by the photodetector allows the location identification of the photodetector position and supplies indoor navigation.

A Combined Location Method for Mobile Robots Based on Dead Reckoning and WLAN

2014 ◽  
Vol 631-632 ◽  
pp. 649-653 ◽  
Author(s):  
Fang Jia ◽  
Kui Liu ◽  
De Cheng Xu
Keyword(s):  
Mobile Robots ◽  
Information Fusion ◽  
Low Cost ◽  
Dead Reckoning ◽  
Probability Matching ◽  
Location Method ◽  
Indoor Location ◽  
Location Algorithm ◽  
Location Methods ◽  
Received Signal Strength Indication

To minimize the deficiency of the existing indoor location methods for mobile robots, the RSSI (received signal strength indication) model of WLAN is established. Then a combined location method for mobile robots based on DR (dead reckoning) and WLAN is proposed, which employs PMLA (probability matching location algorithm) and KF (Kalman filter) for information fusion. Simulation results reveal that the combined location approach works well in eliminating the cumulative error of DR and reducing the fluctuation of WLAN location. As a result, the proposed method is capable of enhancing the positioning accuracy of mobile robots to a certain extent, promising a low-cost and reliable location scheme for its development.

Indoor Location Algorithm Based on Kalman Filter and Multi-Source Data Integration

2016 ◽  
Vol 9 (4) ◽  
pp. 45-54
Author(s):  
Zhang Ya-qiong ◽  
Li Zhao-xing ◽  
Li Xin ◽  
Lv Zhihan-han
Keyword(s):  
Kalman Filter ◽  
Data Integration ◽  
Indoor Location ◽  
Source Data ◽  
Location Algorithm

Indoor Location Algorithm Based on Kalman Filter

2016 ◽  
Author(s):  
Ya-qiong Zhang ◽  
Zhao-xing Li ◽  
Xin Li ◽  
Zhihan-han Lv
Keyword(s):  
Kalman Filter ◽  
Indoor Location ◽  
Location Algorithm
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