Analysis and Research of Artificial Intelligence Algorithms in GPS Data

Abstract

With the development of GPS technology, it has been gradually applied in engineering, science and technology and daily life. The requirement of positioning accuracy in these fields is also increasing. Therefore, this paper studies GPS coordinate conversion and positioning technology from the aspect of artificial intelligence algorithm to improve positioning accuracy. Aiming at the problem of coordinate transformation, a method of GPS coordinate transformation based on convolution neural network model is proposed. Firstly, the input GPS raw data is converted into unstructured images, then the model is used to learn the features of the data, and finally the converted coordinate data is output. Aiming at the problem that the traditional vehicle GPS navigation system is easy to be interfered by the external environment and is not conducive to the system tracking and positioning, AdaBoost algorithm is introduced into the navigation system to better solve the problem. GPS lock. The strong classifier can be obtained by iterative practice, and the filter value can be measured accurately during GPS interference. At the same time, the combination of Adaboost method and BP algorithm can help the filter process information and avoid information loss caused by GPS interference. In this way, not only the integrity of information can be guaranteed, but also the stability and accuracy of the system can be guaranteed. The results show that the coordinate transformation method based on convolution neural network has better conversion accuracy than the traditional method, and the navigation method based on Adaboost algorithm can improve the navigation accuracy and optimize the system performance.