北京航空航天大学学报 ›› 2019, Vol. 45 ›› Issue (11): 2133-2138.doi: 10.13700/j.bh.1001-5965.2019.0138

• 论文 • 上一篇    下一篇

PSO选星算法参数分析与改进

王尔申1,2, 杨迪1, 王传云3, 曲萍萍1, 庞涛1, 蓝晓宇1   

  1. 1. 沈阳航空航天大学 电子信息工程学院, 沈阳 110136;
    2. 北京航空航天大学 电子信息工程学院, 北京 100083;
    3. 沈阳航空航天大学 计算机学院, 沈阳 110136
  • 收稿日期:2019-04-01 出版日期:2019-11-20 发布日期:2019-11-30
  • 通讯作者: 王尔申.E-mail:wanges_2016@126.com E-mail:wanges_2016@126.com
  • 作者简介:王尔申 男,博士,教授。主要研究方向:卫星导航、航空电子技术。
  • 基金资助:
    国家自然科学基金(61571309,61703287);中央高校基本科研业务费专项资金(3132016317);辽宁“百千万人才工程”项目(04021407);辽宁省自然科学基金(2019-MS-251);辽宁省教育厅科研项目(L201705,L201716);辽宁省高等学校优秀人才支持计划(LR2016069)

Parameter analysis and improvement of PSO satellite selection algorithm

WANG Ershen1,2, YANG Di1, WANG Chuanyun3, QU Pingping1, PANG Tao1, LAN Xiaoyu1   

  1. 1. School of Electronic and Information Engineering, Shenyang Aerospace University, Shenyang 110136, China;
    2. School of Electronic and Information Engineering, Beihang University, Beijing 100083, China;
    3. School of Computer Science and Engineering, Shenyang Aerospace University, Shenyang 110136, China
  • Received:2019-04-01 Online:2019-11-20 Published:2019-11-30
  • Supported by:
    National Natural Science Foundation of China (61571309,61703287); the Fundamental Research Funds for the Central Universities (3132016317); Liaoning Baiqianwan Talents Program (04021407); Natural Science Foundation of Liaoning Province (2019-MS-251); Scientific Study Project for Liaoning Province Department of Education (L201705,L201716); Liaoning Excellent Talents in University (LR2016069)

摘要: 多星座组合导航提供更多的可用卫星,但也增大接收机计算复杂度,选取部分可见星代替全部可见星进行接收机位置解算成为选星算法研究的热点。粒子群优化(PSO)选星算法将PSO算法引入到选星过程中,该方法能够减少选星时间,实现北斗/GPS组合星座快速选星。研究了该算法的关键参数包括惯性权重因子、加速系数、种群大小等对PSO选星算法性能的影响,并针对搜索过程容易陷入局部最优问题,提出自适应模拟退火粒子群优化(ASAPSO)选星算法,该算法通过引入随适应值大小自适应调整进化参数及结合模拟退火算法调整粒子速度,以增强算法跳出局部极值的能力。采用实际数据对算法进行验证,结果表明:ASAPSO选星算法在保证选星时间的同时,能够提高算法搜索结果的准确性,其性能优于PSO选星算法。

关键词: 多星座组合导航, 北斗卫星导航系统, GPS, 选星, 粒子群优化(PSO), 模拟退火算法

Abstract: Multi-constellation integrated navigation can provide users with more visible satellites; however, the computational complexity of the navigation receiver will also be increased. Therefore, part visible satellites are selected instead of all visible satellites for receiver position solution, which becomes a hot spot in satellite selection algorithm research. The particle swarm optimization (PSO) is introduced into the satellite selection process by the PSO fast satellite selection algorithm. Through this method, not only the time for selecting satellite is reduced, but also the fast selection of the Beidou/GPS integrated constellation is implemented. The influence of the algorithm's key parameters such as inertia weighting factor, acceleration coefficient and population size on the performance of PSO satellite selection algorithm is studied. In addition, since PSO satellite selection algorithm is easy to fall into the local optimum for the search process, the adaptive simulated annealing particle swarm optimization (ASAPSO) algorithm is proposed to optimize the process of satellite selection algorithm. Moreover, the adaptive adjustment of evolutionary parameters with adaptive value, the adjustment of particle velocity in combination with simulated annealing algorithm are introduced in order to enhance the ability of the algorithm to jump out of local extremum. The algorithm is verified by using real navigation data, and the results demonstrate that the ASAPSO algorithm not only can ensure the satellite selection time, but also can improve the accuracy of the search results. Moreover, the performance of the ASAPSO satellite selection algorithm is better than that of the PSO satellite selection algorithm.

Key words: multi-constellation integrated navigation, Beidou satellite navigation system, GPS, satellite selection, particle swarm optimization (PSO), simulated annealing algorithm

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