基于灰狼优化算法的快速选星方法

doi:10.12305/j.issn.1001-506X.2023.05.25

摘要/Abstract

摘要：

针对传统的遍历法无法满足多全球卫星导航系统(global navigation satellite system, GNSS)组合导航选星的实时性需求, 提出了一种基于灰狼优化(grey wolf optimization, GWO)算法的快速选星方法。该算法利用自适应收敛因子和信息反馈机制, 在局部寻优与全局搜索之间实现平衡, 表现出良好的求解性能, 即可以保证在获得理想几何构型的同时大幅减少接收机运算量。经过仿真实验, 分析了参数选取对GWO快速选星算法结果的影响。利用实测数据对所提算法进行验证, 结果表明，所提算法在四系统组合下, 从49颗可见星中选择7颗进行定位时, 与遍历法相比, 几何精度因子(geometric dilution of precision, GDOP)误差仅为1.8%, 而计算效率提高了71.7%。该算法适用于多GNSS组合导航定位不同选星数目的情况, 还可以拓展至区域导航卫星系统。

关键词: 多全球卫星导航系统组合系统, 快速选星, 灰狼优化算法, 几何精度因子, 计算效率

Abstract:

Aiming at the fact that the traditional traversal method cannot meet the real-time requirements of satellite selection for integrated navigation of multi-global navigation satellite system (GNSS), a fast satellite selection method based on grey wolf optimization (GWO) algorithm is proposed. The algorithm uses adaptive regulatory factor and information feedback mechanism to achieve a balance between local optimization and global search, and shows good calculating performance, which can ensure ideal geometric structure and greatly reduce the computation of receiver. After simulation experiments, the influence of parameter selection on GWO fast satellite selection algorithm is analyzed. The experimental data are used to verify the proposed algorithm. The results show that when selecting 7 of the 49 visible satellites for positioning under the four-system combination, the geometric dilution of precision error is 1.8% and the computational efficiency increases by 71.7% compared with traversal method. The algorithm can be applied to multi-GNSS system with different number of satellites, and it can also be extended to regional navigation satellite system.

Key words: multi-global navigation satellite system (GNSS) integrated system, fast satellite selection, grey wolf optimization (GWO) algorithm, geometric dilution of precision, computational efficiency

中图分类号:

P228.1

余德荧, 李厚朴, 纪兵, 边少锋. 基于灰狼优化算法的快速选星方法[J]. 系统工程与电子技术, 2023, 45(5): 1489-1495.

Deying YU, Houpu LI, Bing JI, Shaofeng BIAN. Fast satellite selection method based on grey wolf optimization algorithm[J]. Systems Engineering and Electronics, 2023, 45(5): 1489-1495.

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参考文献 30

6	WANG E S , JIA C Y , QU P P , et al. BDS/GPS integrated navigation satellite selection algorithm based on chaos particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45 (2): 259- 265.
7	许士杰. 双模系统选星算法研究与全球导航性能分析[D]. 合肥: 安徽大学, 2021.
	XU S J. Research on satellite selection algorithm and analysis of global navigation performance for dual mode system[D]. Hefei: Anhui University, 2021.
8	杨薛涛. BDS/GLONASS双系统组合定位关键技术研究[D]. 西安: 西安电子科技大学, 2018.
	YANG X T. Research on key technologies of BDS/GLONASS combined positioning system[D]. Xi'an: Xidian University, 2018.
9	YANG L , GAO J X , LI Z K , et al. New satellite selection approach for GPS/BDS/GLONASS kinematic precise point positioning[J]. Applied Sciences, 2019, 9 (24): 5280. doi: 10.3390/app9245280
10	ZHANG P F . Research on satellite selection algorithm in ship positioning based on both geometry and geometric dilution of precision contribution[J]. International Journal of Advanced Robotic Systems, 2019, doi: 10.1177/1729881419830246
11	LI F C , LI Z K , GAO J X , et al. A fast rotating partition satellite selection algorithm based on equal distribution of sky[J]. Journal of Navigation, 2019, 72 (4): 1053- 1069. doi: 10.1017/S0373463318001145
12	公才赫, 茅旭初, 李少远. 一种BDS/GPS双系统融合导航的快速选星方法[J]. 上海交通大学学报, 2017, 51 (6): 641- 646.
	GONG C H , MAO X C , LI S Y . A fast satellite selection algorithm for BDS/GPS dual navigation system[J]. Journal of Shanghai Jiao Tong University, 2017, 51 (6): 641- 646.
13	王尔申, 孙彩苗, 黄煜峰, 等. 改进粒子群优化的卫星导航选星算法[J]. 北京航空航天大学学报, 2021, 47 (1): 1- 6. doi: 10.3969/j.issn.1005-4561.2021.01.002
	WANG E S , SUN C M , HUANG Y F , et al. Satellite navigation satellite selection algorithm based on improved particle swarm optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47 (1): 1- 6. doi: 10.3969/j.issn.1005-4561.2021.01.002
14	WANG E S , SUN C M , WANG C Y , et al. A satellite selection algorithm based on adaptive simulated annealing particle swarm optimization for the BeiDou navigation satellite system/global positioning system receiver[J]. International Journal of Distributed Sensor Networks, 2021, 17 (7): 15501477211.
15	邱明, 严勇杰, 孙蕊, 等. 基于帝国竞争优化的双目标综合决策选星算法[J]. 北京航空航天大学学报, 2021, 47 (8): 1645- 1655.
	QIU M , YAN Y J , SUN R , et al. Imperialist competitive optimized dual-objective comprehensive decision algorithm for satellite selection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2021, 47 (8): 1645- 1655.
16	洪韵晴. 一种极化反馈ACO的导航卫星选星方法[D]. 合肥: 合肥工业大学, 2020.
	HONG Y Q. A navigation satellites selection method based on ACO with polarized feedback[D]. Hefei: Hefei University of Technology, 2020.
17	XIA N , ZHI Q N , HE M H , et al. A navigation satellite selection algorithm for optimized positioning based on Gibbs sampler[J]. International Journal of Distributed Sensor Networks, 2020, doi: 10.1177/1550147720929620
18	ZHAO D Q , CAI C C , LI L Y . A binary discrete particle swarm optimization satellite selection algorithm with a queen informant for multi-GNSS continuous positioning[J]. Advances in Space Research, 2021, 68 (9): 3521- 3530.
19	朱军, 许士杰, 李凯. 基于几何分布和差分进化的双模导航选星算法[J]. 北京邮电大学学报, 2021, 44 (3): 9- 14.
	ZHU J , XU S J , LI K . Dual-mode navigation satellite selection algorithm based on differential evolution and geometry[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44 (3): 9- 14.
20	MIRJALILI S , MIRJALILI S M , LEWIS A . Grey wolf optimi-zer[J]. Advances in Engineering Software, 2014, 69 (3): 46- 61.
1	杨元喜. 综合PNT体系及其关键技术[J]. 测绘学报, 2016, 45 (5): 505- 510.
	YANG Y X . Concepts of comprehensive PNT and related key technologies[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45 (5): 505- 510.
2	杨元喜. 弹性PNT基本框架[J]. 测绘学报, 2018, 47 (7): 893- 898.
	YANG Y X . Resilient PNT concept frame[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47 (7): 893- 898.
3	DENG Y F , GUO F , REN X D , et al. Estimation and analysis of multi-GNSS observable-specific code biases[J]. GPS Solutions, 2021, 25 (3): 100. doi: 10.1007/s10291-021-01139-6
4	刘一, 周威, 金际航, 等. 基于Mean Shift模型的多粗差探测RAIM算法[J]. 系统工程与电子技术, 2022, 44 (2): 644- 650.
	LIU Y , ZHOU W , JIN J H , et al. RAIM algorithm for multiple gross errors detection based on Mean Shift model[J]. Systems Engineering and Electronics, 2022, 44 (2): 644- 650.
5	WANG H B , CHENG Y M , CHENG C , et al. Research on satellite selection strategy for receiver autonomous integrity monitoring applications[J]. Remote Sensing, 2021, 13 (9): 1725. doi: 10.3390/rs13091725
6	王尔申, 贾超颖, 曲萍萍, 等. 基于混沌粒子群优化的北斗/GPS组合导航选星算法[J]. 北京航空航天大学学报, 2019, 45 (2): 259- 265.
21	杨叶枫. 基于灰狼算法的制造云服务组合优化方法研究[D]. 重庆: 重庆大学, 2020.
	YANG Y F. Study on grey wolf optimizer for service composition optimization in cloud manufacturing[D]. Chongqing: Chongqing University, 2020.
22	张晓凤, 王秀英. 灰狼优化算法研究综述[J]. 计算机科学, 2019, 46 (3): 30- 38.
	ZHANG X F , WANG X Y . Comprehensive review of grey wolf optimization algorithm[J]. Computer Science, 2019, 46 (3): 30- 38.
23	王伟. 灰狼优化算法及其在图像增强中的应用[D]. 北京: 北京工业大学, 2020.
	WANG W. Grey wolf optimization algorithm and its application in image enhancement[D]. Beijing: Beijing University of Technology, 2020.
24	陈闯, 陆宁云, 姜斌, 等. 单部件加速退化系统的视情维修策略优化[J]. 系统工程与电子技术, 2020, 42 (3): 613- 619.
	CHEN C , LU N Y , JIANG B , et al. Optimization of condition-based maintenance strategy for single-unit accelerated degrading systems[J]. Systems Engineering and Electronics, 2020, 42 (3): 613- 619.
25	黄晨晨. 基于灰狼优化算法的SLAM数据关联算法的研究[D]. 乌鲁木齐: 新疆大学, 2021.
	HUANG C C. Research on simultaneous localization and mapping data association algorithm based on grey wolf optimization algorithm[D]. Urumqi: Xinjiang University, 2021.
26	PRERNA S , ASHWIN K , FELIPE C . Optimal pattern synthesis of linear antenna array using grey wolf optimization algorithm[J]. International Journal of Antennas and Propagation, 2016, 2016, 1205970.
27	于添铎. 基于灰狼算法的圆形阵列天线优化设计研究[D]. 哈尔滨: 哈尔滨工程大学, 2021.
	YU T D. Research on circular arrays antenna optimization and design based on grey wolf optimizer[D]. Harbin: Harbin Engineering University, 2021.
28	韩驰, 熊伟. 基于改进灰狼算法优化SVR的航天侦察装备效能评估[J]. 系统工程与电子技术, 2021, 43 (10): 2902- 2910.
	HAN C , XIONG W . Operational effectiveness evaluation of space reconnaissance equipment based on SVR optimized by improved grey wolf optimizer[J]. Systems Engineering and Electronics, 2021, 43 (10): 2902- 2910.
29	LI Y L , ZHOU L W , GAO Z D , et al. Optimal dispatch of multi-virtual power plants based on grey wolf optimization algorithm[J]. Journal of Physics: Conference Series, 2021, 2005 (1): 012080.
30	ZHAO H Y , JIN J , SHAN B J , et al. Pulsar identification method based on adaptive grey wolf optimization algorithm in X-ray pulsar-based navigations[J]. Advances in Space Research, 2022, 69 (2): 1220- 1235.

迭代次数	平均GDOP	最大GDOP	最小GDOP	平均耗时/s
20	2.541 7	2.964 3	2.265 1	1.212 5
40	2.488 6	2.783 8	2.268 6	2.427 6
60	2.461 8	2.681 9	2.300 4	3.694 8
80	2.423 3	2.614 8	2.265 7	4.978 1
100	2.399 6	2.571 0	2.186 7	6.310 4

狼群个体数	平均GDOP	最大GDOP	最小GDOP	平均耗时/s
20	2.438 7	2.730 0	2.227 4	2.870 8
40	2.434 0	2.667 1	2.272 1	4.085 9
60	2.414 1	2.601 1	2.186 7	5.200 0
80	2.437 5	2.642 0	2.194 3	6.200 5
100	2.417 3	2.620 7	2.238 4	8.975 5

选星数目	遍历法		GWO算法
选星数目	GDOP	计算耗时/s	GDOP	计算耗时/s
4	2.637 7	1.427 7	2.768 8	4.126 1
5	2.478 7	4.908 1	2.626 5	4.785 9
6	2.445 5	8.093 5	2.626 4	5.666 3
7	2.445 5	21.912 7	2.488 9	6.206 7

[1]	安雷, 李召瑞, 吉兵. 杂波环境下可移动主被动传感器长时调度方法[J]. 系统工程与电子技术, 2023, 45(1): 165-174.
[2]	韩驰, 熊伟. 基于改进灰狼算法优化SVR的航天侦察装备效能评估[J]. 系统工程与电子技术, 2021, 43(10): 2902-2910.
[3]	王文博, 徐颖. 基于稳健MM估计的REKF RAIM算法[J]. 系统工程与电子技术, 2021, 43(1): 216-222.
[4]	杨志涛, 刘静, 刘林. 轨道分析解的改进方法及其应用[J]. 系统工程与电子技术, 2020, 42(2): 427-433.
[5]	卢雨, 周正. 空基外辐射源定位系统的观测站航迹优化[J]. 系统工程与电子技术, 2020, 42(12): 2708-2715.
[6]	王晴昊, 姚登凯, 胡剑波, 赵顾颢, 李宁. 远距离支援最优干扰空域规划[J]. 系统工程与电子技术, 2019, 41(4): 835-842.
[7]	杨一，高社生，阎海峰. 临近空间伪卫星几何布局方案设计[J]. 系统工程与电子技术, 2014, 36(3): 532-538.
[8]	雷雨, 冯新喜, 朱灿彬, 李彬彬. 2D雷达组网几何定位融合算法[J]. Journal of Systems Engineering and Electronics, 2011, 33(5): 1151-.