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In-Depth Analysis of Unmodulated Visible Light Positioning Using the Iterated Extended Kalman Filter.
Robin Amsters1, Eric Demeester1, Nobby Stevens2
1KU Leuven, Department of Mechanical Engineering, 3000 Leuven, Belgium.
Sensors (Basel, Switzerland)
|December 1, 2019
Summary
This study explores using unmodulated visible light for indoor positioning, reducing hardware costs. A Kalman filter enhances position estimation speed and reliability, even with temporary signal interruptions.
Area of Science:
- Electrical Engineering
- Robotics
- Computer Science
Background:
- Indoor positioning systems (IPS) using visible light are gaining importance.
- Current systems often require modulated light sources, increasing cost and complexity.
- High-speed modulation is typically used for data transmission in visible light communication (VLC).
Purpose of the Study:
- To investigate the feasibility of indoor positioning using unmodulated visible light.
- To reduce the hardware requirements and system complexity of visible light positioning systems.
- To improve the computational efficiency and update rate of indoor positioning.
Main Methods:
- Utilized unmodulated light sources for indoor positioning.
- Implemented a Kalman filter for position estimation, replacing traditional particle filters.
- Evaluated the proposed approach through both simulations and experimental validation.
Main Results:
- Achieved an indoor positioning accuracy generally lower than 0.5 meters.
- The Kalman filter decreased computational load, allowing for higher position estimation update rates.
- The system demonstrated the ability to compensate for temporary receiver occlusion.
Conclusions:
- Indoor positioning with unmodulated visible light is a viable and cost-effective alternative.
- Kalman filtering offers computational advantages over particle filters for this application.
- The proposed method supports efficient positioning, suitable for tasks like autonomous robot navigation.
