Noise filtering, trajectory compression and trajectory segmentation on GPS data
Kunhui Lin, Zhentuan Xu, Ming Qiu, Xiaoli Wang, Tianxiong Han · 2016
With the rapid development of GPS devices, satellite and wireless communications technologies, many trajectory data is generated. Consequently, processing and analyzing trajectory data have become a hot topic. In this paper, an improved noise filtering, trajectory compression and trajectory segmentation method based on Kalman filter and Douglas-Peucker algorithm and corner is proposed. Firstly, the Kalman filter is used to filter noise in the trajectory and making a mark in the points whose direction change are greater than threshold. Secondly, combine the Douglas-Peucker algorithm with the Sliding Window algorithm to approximate the trajectory. Thirdly, segmenting the trajectory into trajectory segmentation set according to the corner and predefined corner threshold. Experiments on real dataset demonstrate the efficiency and effectiveness of the improved method, getting a good noise filtering and trajectory segment results and have practical significance.