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.
Highlights
Nowadays, GPS technology is developing rapidly and its technology is becoming more and more mature
The results show that the seven-parameter model has better conversion accuracy
As a non-linear mapping system that can be approximated with arbitrary precision [13], neural network has been applied to the field of GPS coordinate transformation by more and more scholars this year
Summary
GPS technology is developing rapidly and its technology is becoming more and more mature. The emphasis of the Adaboost algorithm is to analyze the weight of the results of the first exercise, rearrange the samples, train the new samples to get many weak exercisers, and combine them into a strong exerciser to reduce the system error and improve the accuracy of the system[16][18] Wu Zhengjiang[19] and others combined Adaboost algorithm with BP neural network algorithm to solve the problem of easy paralysis and slow convergence speed of traditional BP algorithm under large-scale data, which accelerated the speed of system operation and increased the accuracy of the system. Aiming at the problem that the accuracy of navigation system decreases when GPS is disturbed, the Adaboost method is introduced into BP neural network control. The strong learner is trained to compensate the navigation information lost during the disturbance of GPS and improve the accuracy and stability of the system [34]
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