Prediction of WiFi Signal using Kalman Filter for Fingerprinting-based Mobile Robot Wireless Positioning System

Authors

  • A.H. Ismail School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus 02600 Arau Perlis, MALAYSIA. System and Control Laboratory, Department of Mechanical Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka Tempaku-cho Toyohashi, Japan.
  • K. Terashima System and Control Laboratory, Department of Mechanical Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka Tempaku-cho Toyohashi, Japan.

Keywords:

Positioning Systems, Fingerprinting Technique, Wireless Localization, Kalman Filters, Robotics,

Abstract

The non-predictive behaviour of wireless signal especially that of 2.4GHz WiFi due to the complex signal propagation is primarily non-usable for mobile robot positioning system. It is fluctuating and prone to error that made positioning accuracy haywires even in stationary location. Therefore, there is a need to estimate the wireless signal to its real value as per fingerprint location. This paper proposed to use the Linear Kalman Filter (LKF) to predict the wireless signal, i.e. the WiFi Received Signal Strength (RSS) to estimate the location using the Weighted K-Nearest Neighbor (WKNN) algorithm that matched the fingerprinting database constructed beforehand. By employing the LKF, the accuracy of the positioning system at any stationary location has improved significantly when compared to the use of raw original WiFi signal.

References

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Published

2018-05-30

How to Cite

Ismail, A., & Terashima, K. (2018). Prediction of WiFi Signal using Kalman Filter for Fingerprinting-based Mobile Robot Wireless Positioning System. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-15), 17–21. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4039

Issue

Vol. 10 No. 1-15: Exploring Man-Machines Compliance in Robotics and Image Processing

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Articles

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