GPS positioning algorithm based on Kalman filter and improved DBSCAN clustering combination

Real-time acquisition of geographic location information of smart mobile terminals is the key to the realization of an augmented reality (AR) real-scene smart navigation system. In order to improve the accuracy of GPS positioning for smart terminals, a GPS combined positioning optimization method based on Kalman filtering and improved DBSCAN clustering algorithm is proposed. Kalman filtering is performed on the position coordinate data collected by the GPS system to remove large data fluctuations and control the positioning error range. Using DBSCAN clustering algorithm for classification denoising and secondary clustering, the arithmetic mean value of the data in the class and the total number of data between the classes are weighted to find the center of gravity, and the position coordinates are determined. The experimental results show that the proposed algorithm can effectively improve the GPS single-point positioning accuracy, reduce positioning errors, and at the same time well meet the real-time and robustness requirements of the AR real-world intelligent navigation system.