• 论文 •

### 基于帝国竞争优化的双目标综合决策选星算法

1. 1. 南京航空航天大学 民航学院, 南京 211106;
2. 空中交通管理系统与技术国家重点实验室, 南京 210007
• 收稿日期:2020-06-02 发布日期:2021-09-06
• 通讯作者: 孙蕊 E-mail:rui.sun@nuaa.edu.cn
• 基金资助:
国家自然科学基金（41704022，41974033）；空中交通管理系统与技术国家重点实验室开放基金（SKLATM201904）；江苏省自然科学基金（BK20170780）；中央高校基本科研业务费专项资金（KFJJ20190727）

### Imperialist competitive optimized dual-objective comprehensive decision algorithm for satellite selection

QIU Ming1,2, YAN Yongjie2, SUN Rui1, ZHANG Wenyu1

1. 1. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China;
2. State Key Laboratory of Air Traffic Management System and Technology, Nanjing 210007, China
• Received:2020-06-02 Published:2021-09-06
• Supported by:
National Natural Science Foundation of China (41704022,41974033); States Key Laboratory of Air Traffic Management System and Technology (SKLATM201904);Natural Science Foundation of Jiangsu Province (BK20170780); the Fundamental Research Funds for the Central Universities (KFJJ20190727)

Abstract: With the development of Global Navigation Satellite System(GNSS), the prospect of GNSS has been widely recognized in the world. In particular, the positioning solutions with fast and accuracy calculation are essential for the GNSS receiver design. The most of the current satellite selection algorithms in the GNSS receiver fix the number of satellites in advance, which limits the performance of the algorithm. This paper proposes an Imperialist Competitive Algorithm (ICA) for satellite selection. In order to obtain better geometric configuration of satellite constellation, the prior information (elevation and azimuth of visible satellite) is introduced for prior constraint. The Geometric Dilution of Precision (GDOP) and number of satellites are two objectives of the optimization algorithm. Comprehensive decisions are used to quickly select satellites, making the selection of satellites more flexible, as well as reducing the computational burden of multi-constellation satellite receivers. Experiment results based on simulation and field data showed that, after priori constraints are introduced, at elevation angle 5°, the average number of satellites selected by the algorithm proposed in this paper is 51.8% of the maximum visible satellites based on simulation data and the average number of satellites is 45.4% of the maximum visible satellites based on field data. The average GDOP is decreased by 0.209 2 and 0.248 4 compared to the satellite selection without a priori constraint. At the same time, the average calculation time for once satellite selection is about 0.168 4 s and 0.303 1 s, with an improvement of 95.79% and 92.42% compared to the time consumption (i.e. 4 s) of the traversal method.