The Model Research on Location Fusion Algorithm with Big Data Selection and Accuracy Correction

Yini Zhang, Ping Zhao, Yuanwei Zhao, Zhongjiang Yan, Bo Li


As the requirements of smart, reliable and precise location for a vehicle, the model of fusion location algorithm with big data selection and accuracy correction is established to achieve reliable and low-cost fusion location. In this paper, the data of simple inertial navigation and the data of various positioning system sources with different errors are intelligently selected, and Kalman filtering is used to fuse the location information by the function of algorithm model, and the four kinds of location information of GPS, SINS, DR and TDOA are chosen to simulate fusion algorithm. The simulation results in MATLAB environment show that the proposed fusion location algorithm model is efficient to improve the accuracy and reliability of the location algorithm.


Combined Location; Fusion Algorithm Model; Big Data Selection; Kalman Filtering


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