Advanced visible light-based indoor positioning techniques : development and analysis

Abstract

In recent years, light-emitting diodes (LEDs) enabled indoor visible light positioning (VLP) technology has attracted ever-increasing attention, due to its numerous potential applications including pedestrian localization and navigation, robots monitoring, assets tracking and even industrial manufacturing. Compared with traditional radio-frequency (RF)-based systems (e.g., Wi-Fi, Bluetooth), LED-based VLP systems has many inherent advantages, such as energy efficiency, long life-time, better positioning accuracy, etc. Besides, it can be integrated with visible light communication (VLC) system which is a fast-developing next-generation wireless communication system. Nevertheless, the development of accurate, stable and low-cost indoor VLP systems faces many challenging issues: i) limited mobility and robustness due to the line-of-sight constraint; ii) conflict of the complex processing and positioning algorithms with the limited memory, power and calculation ability of mobile devices; iii) high positioning accuracy to centimeter, even to millimeter level; iv) interruption/outage during the localization; v) the integration of VLC and VLP systems. This thesis addresses part of the pressing issues mentioned above to improve the overall performance of a VLP system. Both photodiodes (PDs) and camera/image sensors (ISs) are commonly used as the receivers of a VLP system. In an IS-based VLP system, a target’s location is calculated by solving a camera projection model which describes the one-one correlation between an object point and its projected image point. However, the conventional iterative solvers, e.g., Levenberg-Marquardt (LM), which are used to solve the projection model, are found sensitive to the initial guesses. It means the algorithm may fail to converge or suffer a long response time and a large positioning error when starting from a bad guess. To speed up the positioning process and guarantee the success rate, we derive a closed-form solution to the receiver’s position and orientation using singular value decomposition (SVD) technique and propose an SVD based positioning algorithm. Extensive tests verify that the proposed algorithm is 50-80 times faster than the conventional LM algorithm and avoids convergence failures caused by the bad initial guesses. Meanwhile, we derive the Cramer-Rao lower bound (CRLB) as the theoretical accuracy limit. Simulations are performed to reveal the impact of the various system parameters on the positioning error bound. A robust and fast 3D IS-based VLP system with centimeter-to-decimeter level accuracy is demonstrated experimentally. On the basis of the above IS-based VLP system, a sensor fusion-based positioning algorithm is proposed to enhance positioning accuracy by effectively fusing data from the image sensor and the motion sensors, both of which are widely accessible in common mobile devices. The issue of sensor fusion is formulated as a multi-objective non-convex optimization problem and mathematically solved using SVD technique, again. Experiment results show that the proposed singular value decomposition-based sensor fusion (SVD-SF) algorithm brings approximate 44% positioning accuracy improvement compared with the algorithm employing a single image sensor under the same experimental settings. Simulations are conducted to further evaluate its positioning error performance under different noises conditions. The IS-based VLP systems mentioned above requires capturing at least three LEDs in every picture to calculate the targets’ positions, otherwise positioning is interrupted or fail. The requirement greatly reduces the system robustness and flexibility. To deal with this problem, we propose a single circular LED positioning (SCLP) system where a common circular LED lamp with a point marker is used as a transmitter. The LED image is no longer treated as a point as in the existing works, but as an image whose geometric features are exploited to determine the receiver’s orientation