| Recently, due to the shortcoming of the indoor positioning system based on radio frequency technology, the system can’t meet the requirement of indoor location. However, with the arrival of the LED lighting, visible light communication(VLC) has been developing rapidly. And compared with the radio frequency communication, the VLC has many significant advantages. So indoor positioning based on VLC has received considerable attention recently.The main contents of this thesis are as follows:Firstly, the thesis introduces some classic indoor positioning theories such as TOA, TDOA, RSS and DOA, and then describes the differences between each method. We have gained a useful insight of these methods, which provides the theoretical foundation for later research on indoor positioning based on VLC. Then the channel model of VLC is described.Secondly, the RSS-based VLC indoor positioning system is analyzed. To improve the performance of the system, this thesis proposes a method utilizing RSS and AOA positioning algorithm, and the algorithm improves the positioning accuracy in a certain extent. Then, the kalman filter algorithm is applied to obtain higher positioning accuracy of mobile devices on the edge of the indoor areas.Then, for the shortcomings of the RSS and AOA algorithm used in the indoor areas, this thesis studies a positioning method based on RSS fingerprint. This method can make up for the defects of the parameterized positioning methods, and it can reduce the multipath effects as well. Hence, it has a higher positioning accuracy.Lastly, with the purpose of reducing the influence of environmental factors such as multipath effect, this thesis studies an indoor positioning system based on sparse signal recovery. This method applies probabilistic signal model that takes into account disturbances to achieve the robustness of the system. In order to further improve the positioning accuracy of the system, the K Nearest Neighbor algorithm is applied in the system. |
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