BDS/GPS integrated navigation satellite selection algorithm based on chaos particle swarm optimization
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摘要:
全球卫星导航系统（GNSS）接收机在接收信号的过程中会受到诸如建筑物遮挡、信号干扰等因素的影响，无法得到全部可见星。为减轻多星座组合接收机的处理负担，研究利用部分可见卫星进行定位的快速选星算法，提出了一种基于混沌粒子群优化（CPSO）的北斗/GPS组合导航选星算法。首先，对当前历元时刻可见卫星进行连续编码，按照选星数目分组，每个分组视为一个粒子。然后，通过混沌映射初始化粒子种群，选取几何精度因子（GDOP）作为评价粒子优劣的适应度函数；粒子通过粒子群优化算法的速度-位移模型更新自身位置，逐渐趋近空间卫星几何分布较好的卫星组合全局最优解。最后，采集北斗/GPS实际数据对选星算法进行仿真验证和性能比较，结果表明，所提算法在选星颗数多于5颗时，单次选星耗时为遍历法选星的37.5%，选星结果的几何精度因子计算误差在0~0.6之间。该算法可适用于北斗/GPS组合导航定位不同选星颗数的情况。
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关键词:
- 北斗/GPS组合导航 /
- 选星 /
- 混沌粒子群优化(CPSO) /
- 几何精度因子(GDOP) /
- 适应度函数
Abstract:In the process of signal receiving, global navigation satellite system (GNSS) receiver will be affected by factors such as building blockages and signal interference and will not be able to obtain all the visible satellites; moreover, in order to reduce the processing burden of multi-constellation receivers, the fast satellite selection algorithm using partial visible satellites to achieve positioning solution is investigated, and the BDS/GPS integrated navigation satellite selection algorithm based on chaos particle swarm optimization (CPSO) is proposed. First, the visible satellites are continuously numbered and randomly divided into groups. Each group is regarded as a particle. Then, chaotic maps are used to select several groups from all grouping spaces to form initial population. The geometric dilution of precision (GDOP) is chosen as fitness function to evaluate the particle's quality. In addition, the particle's position is updated by the velocity-displacement model of the PSO algorithm, and it gradually approaches the global optimal solution of the satellite combination with better geometric distribution of the space satellite. Finally, using real navigation data, the algorithm is verified by simulation experiments. The results demonstrate that when the number of selected satellite is more than 5, the time that the proposed algorithm takes to select satellite once is 37.5% of the time that the traversing algorithm takes, and the GDOP error of the selected satellites is between 0 and 0.6. Moreover, the proposed algorithm can be applied to the case of different numbers of selected satellite in BDS/GPS integrated navigation.
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图 2 北斗/GPS可见卫星数及对应的GDOP最小值
Figure 2. Number of BDS/GPS visible satellite and their minimum GDOP
图 3 PSO选星时GDOP值随迭代次数的变化
Figure 3. Change of GDOP with iteration numbers of PSO satellite selection
图 4 CPSO选星时GDOP值随迭代次数的变化
Figure 4. Change of GDOP with iteration numbers of CPSO satellite selection
表 1 三种选星算法性能对比
Table 1. Performance comparison of three satellite selection algorithms
选星算法 单次耗时/s GDOP 选星结果 遍历法 4.074 824 2.251 038 92127373839 PSO 1.665 020 2.347 418 92127373842 CPSO 1.435 994 2.333 044 92127333837 
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[1] 张军.空地协同的空域监视新技术[M].北京:航空工业出版社, 2011:36-38.ZHANG J.Air-ground collaborative airspace surveillance[M].Beijing:Aviation Industry Press, 2011:36-38(in Chinese). [2] 王尔申, 杨福霞, 庞涛, 等.BDS/GPS组合导航接收机自主完好性监测算法[J].北京航空航天大学学报, 2018, 44(4):684-690.WANG E S, YANG F X, PANG T, et al.BDS/GPS combined navigation receiver autonomous integrity monitoring algorithm[J].Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(4):684-690(in Chinese). [3] ZHANG M, ZHANG J.A fast satellite selection algorithm:Beyond four satellites[J].IEEE Journal of Selected Topics in Signal Processing, 2009, 3(5):740-747. doi: 10.1109/JSTSP.2009.2028381 [4] SWASZEK P F, HARTNETT R J, SEALS K C, et al.Multi-constellation GNSS: New bounds on DOP and a related satellite selection process[C]//Proceedings of the 29th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+2016).Washington, D.C.: INST Navigation, 2016: 228-235. [5] 丛丽, AHMED I A, 谈展中.卫星导航几何因子的分析和仿真[J].电子学报, 2006, 34(12):2204-2208. doi: 10.3321/j.issn:0372-2112.2006.12.017CONG L, AHMED I A, TAN Z Z.Analysis and simulation of the GDOP of satellite navigation[J].Acta Electronica Sinica, 2006, 34(12):2204-2208(in Chinese). doi: 10.3321/j.issn:0372-2112.2006.12.017 [6] 陈灿辉, 张晓林.一种新的卫星导航系统快速选星方法[J].电子学报, 2010, 38(12):2887-2891.CHEN C H, ZHANG X L.A fast satellite selection approach for satellite navigation system[J].Acta Electronica Sinica, 2010, 38(12):2887-2891(in Chinese). [7] PHATAK M S.Recursive method for optimum GPS satellite selection[J].IEEE Transactions on Aerospace & Electronic Systems, 2001, 37(2):751-754. [8] MOSAVI M R, DIVBAND M.Calculation of geometric dilution of precision using adaptive filtering technique based on evolutionary algorithms[C]//International Conference on Electrical and Control Engineering.Piscataway, NJ: IEEE Press, 2010: 4842-4845. [9] 宋丹, 许承东, 胡春生, 等.基于遗传算法的多星座选星方法[J].宇航学报, 2015, 36(3):300-308. doi: 10.3873/j.issn.1000-1328.2015.03.008SONG D, XU C D, HU C S, et al.Satellite selection with genetic algorithm under multi-constellation[J].Journal of Astronautics, 2015, 36(3):300-308(in Chinese). doi: 10.3873/j.issn.1000-1328.2015.03.008 [10] 霍航宇, 张晓林.组合卫星导航系统的快速选星方法[J].北京航空航天大学学报, 2015, 41(2):273-282.HUO H Y, ZHANG X L.Fast satellite selection method for integrated navigation systems[J].Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(2):273-282(in Chinese). [11] 徐小钧, 马利华, 艾国祥.基于多目标遗传算法的多星座选星方法[J].上海交通大学学报, 2017, 51(12):1520-1528.XU X J, MA L H, AI G X.Satellite selection with multi-objective genetic algorithm for multi-GNSS constellations[J].Journal of Shanghai Jiao Tong University, 2017, 51(12):1520-1528(in Chinese). [12] EBERHART R C, SHI Y H.Particle swarm optimization: Developments, applications and resources[C]//Proceedings of the 2001 Congress on Evolutionary Computation.Piscataway, NJ: IEEE Press, 2002: 81-86. [13] EBERHART R C, KENNEDY J.A new optimizer using Particle swarm theory[C]//Proceeding of the 6th International Symposium on Micro Machine and Human Science.Piscataway, NJ: IEEE Press, 1995: 39-43. [14] SHI Y H, EBERHAR R.A modified particle swarm optimizer[C]//IEEE International Conference on Evolutionary Computation.Piscataway, NJ: IEEE Press, 1998: 69-73. [15] 胥小波, 郑康锋, 李丹, 等.新的混沌粒子群优化算法[J].通信学报, 2012, 33(1):24-30. doi: 10.3969/j.issn.1000-436X.2012.01.004XU X B, ZHENG K F, LI D, et al.New chaos-particle swarm optimization algorithm[J].Journal on Communications, 2012, 33(1):24-30(in Chinese). doi: 10.3969/j.issn.1000-436X.2012.01.004 [16] TIAN D P, SHI Z Z.MPSO:Modified particle swarm optimization and its applications[J].Swarm & Evolutionary Computation, 2018, 41:49-68. 期刊类型引用(24)
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