A New Satellite Selection Algorithm Based on K-means

Jian Tang, Fangling Zeng, Tianbao Dong, Daqian Lv
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Abstract

The first step is to select satellites during navigation and positioning, which determines the accuracy of navigation and positioning directly. A new satellite selection algorithm based on K-means clustering is proposed by calculating the azimuth and elevation of satellites in order to improve the accuracy and the real-time performance of the algorithm. On the basis of selecting the satellite with the highest elevation angle, this paper uses K-means algorithm to cluster the azimuth angle to select satellites and uses MATLAB software to simulate. The simulation results show that the GDOP of the algorithm is almost the same as that obtained by the optimal GDOP method, which has high accuracy, while the computational complexity of the algorithm is greatly reduced and the timeliness of satellite selection is improved.
一种新的基于k均值的卫星选择算法
第一步是导航定位时的卫星选择,这直接决定了导航定位的精度。为了提高算法的精度和实时性,提出了一种基于k均值聚类的卫星选择算法,通过计算卫星的方位角和仰角。本文在选择仰角最高的卫星的基础上,采用K-means算法对方位角进行聚类选择卫星,并使用MATLAB软件进行仿真。仿真结果表明,该算法的GDOP与最优GDOP方法得到的GDOP基本相同,具有较高的精度,同时大大降低了算法的计算复杂度,提高了卫星选择的时效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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