-
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。
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.
-
图 1 PSO和ASAPSO选星算法GDOP变化
Figure 1. GDOP changes in PSO and ASAPSO satellite selection algorithm
图 2 PSO、改进PSO及ASAPSO选星算法的GDOP计算误差
Figure 2. GDOP calculation error of PSO, improved PSO and ASAPSO satellite selection algorithm
表 1 惯性权重对PSO选星算法性能的影响
Table 1. Effect of inertia weight on PSO satellite selection algorithm performance
惯性权重
ω平均
GDOP最大
GDOP最小
GDOP平均选星耗时/s 0.4 2.320 4 2.364 5 2.251 0 1.517 0.6 2.314 5 2.419 6 2.251 0 1.566 0.8 2.318 5 2.358 5 2.251 0 1.546 0.9 2.288 0 2.419 6 2.251 0 1.548 1.0 2.345 7 2.370 7 2.251 0 1.527 1.1 2.299 8 2.481 1 2.251 0 1.626 1.2 2.327 3 2.540 2 2.251 0 1.538 1.3 2.343 4 2.370 7 2.251 0 1.561 1.4 2.352 9 2.460 5 2.251 0 1.544 1.6 2.342 5 2.426 9 2.251 0 1.539 
下载: 导出CSV
表 2 加速系数对算法性能的影响
Table 2. Effect of acceleration factor on algorithm performance
加速系数 c1/c2 平均
GDOP最大
GDOP最小
GDOP平均选星耗时/s c1=1，c2=4 0.25 2.339 4 2.380 1 2.251 0 1.559 c1=1，c2=2 0.5 2.348 3 2.540 2 2.251 0 1.573 c1=1，c2=1 1 2.355 1 2.419 6 2.251 0 1.616 c1=1.5，c2=1.5 1 2.381 9 2.540 2 2.251 0 1.566 c1=2，c2=2 1 2.295 1 2.347 4 2.251 0 1.569 c1=0.5，c2=0.5 1 2.403 5 2.540 2 2.251 0 1.557 c1=0.25，c2=0.25 1 2.357 7 2.509 9 2.251 0 1.578 c1=4，c2=2 2 2.298 5 2.370 7 2.251 0 1.573 c1=2，c2=1 2 2.308 3 2.419 6 2.251 0 1.638 c1=1，c2=0.5 2 2.366 2 2.419 6 2.251 0 1.565 c1=3，c2=1 3 2.329 4 2.460 5 2.251 0 1.579 c1=4，c2=1 4 2.317 9 2.370 7 2.251 0 1.581
下载: 导出CSV
表 3 种群规模对算法性能的影响
Table 3. Effect of population sizes on algorithm performance
种群规模M 平均
GDOP最大
GDOP最小
GDOP平均选星耗时/s 30 2.405 439 541 2.509 928 411 2.251 0 0.546 194 8 50 2.332 476 761 2.419 611 127 2.2510 0.828 243 7 70 2.289 530 344 2.364 511 595 2.251 0 1.127 988 2 90 2.308 514 516 2.358 491 188 2.251 0 1.441 279 7 100 2.292 758 802 2.419 611 127 2.251 0 1.586 975 4 110 2.317 243 548 2.419 611 127 2.251 0 1.793 920 8 120 2.326 449 813 2.540 188 469 2.251 0 1.924 731 8 150 2.312 394 223 2.370 668 208 2.251 0 2.491 832 2 180 2.283 993 701 2.334 593 796 2.251 0 2.775 929 5 200 2.306 857 579 2.370 668 208 2.251 0 3.123 896 5
下载: 导出CSV
-
[1] DEANE B, LEO E, DEBORAH L.GNSS evolutionary architecture study Phase Ⅱ Report[R]. Washington, D.C.: FAA, 2010. [2] ZHENG Z Y, HUANG C, FENG C G, et al.Selection of GPS satellites for the optimum geometry[J]. Chinese Astronomy and Astrophysics, 2004, 28:80-87. doi: 10.1016/S0275-1062(04)90009-4 [3] DONG S H.A closed-form formula for GPS GDOP computation[J]. GPS Solutions, 2009, 13(3):183-190. doi: 10.1007/s10291-008-0111-2 [4] 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. https://bhxb.buaa.edu.cn/CN/abstract/abstract13160.shtmlHUO 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). https://bhxb.buaa.edu.cn/CN/abstract/abstract13160.shtml [5] BLANCO-DELGADO N, NUNES F D.Satellite selection method for multi-constellation GNSS using convex geometry[J]. IEEE Transactions on Vehicular Technology, 2010, 59(9):4289-4297. doi: 10.1109/TVT.2010.2072939 [6] 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 [7] WANG E S, JIA C Y, TONG G, et al.Fault detection and isolation in GPS receiver autonomous integrity monitoring based on chaos particle swarm optimization-particle filter algorithm[J]. Advances in Space Research, 2018, 61(9):1260-1272. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=cd00e7c21838dafba7cdf21d45282e3d [8] 冯智博, 黄宏光, 李奕.基于改进粒子群算法的WSN覆盖优化策略[J].计算机应用研究, 2011, 28(4):1272-1275. doi: 10.3969/j.issn.1001-3695.2011.04.020FENG Z B, HUANG H G, LI Y.Strategy of wireless sensor networks coverage optimization by improved particle swarm algorithm[J]. Application Research of Computers, 2011, 28(4):1272-1275(in Chinese). doi: 10.3969/j.issn.1001-3695.2011.04.020 [9] 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. https://bhxb.buaa.edu.cn/CN/abstract/abstract14711.shtmlWANG E S, JIA C Y, QU P P, et al.Research on BDS/GPS integrated navigation fast selection algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(2):259-265(in Chinese). https://bhxb.buaa.edu.cn/CN/abstract/abstract14711.shtml [10] 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 [11] SHI Y H, EBERHART R C.A modified particle swarm optimizer[C]//IEEE International Conference on Evolutionary Computation.Piscataway, NJ: IEEE Press, 1998: 69-73. http://www.researchgate.net/publication/3755900_Modified_particle_swarm_optimizer [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. http://www.researchgate.net/publication/247116719_particle_swarm_optimization_developments [13] KURU L, OZTURK A, KURU E, et al.Determination of voltage stability boundary values in electrical power systems by using the chaotic particle swarm optimization algorithm[J]. International Journal of Electrical Power & Energy Systems, 2015, 64(15):873-879. http://cn.bing.com/academic/profile?id=b41afefa2948ab37aba8fb44e989b94d&encoded=0&v=paper_preview&mkt=zh-cn [14] WU G, WANG H, PEDRYCZ W, et al.Satellite observation scheduling with a novel adaptive simulated annealing algorithm and a dynamic task clustering strategy[J]. Computers & Industrial Engineering, 2017, 113:576-588. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=ebbcff09ea91fd3eb936cccc3985ccc6 [15] ASSAD A, DEEP K.A hybrid harmony search and simulated annealing algorithm for continuous optimization[J]. Information Sciences, 2018, 450:246-266. doi: 10.1016/j.ins.2018.03.042 [16] TAVARES R S, MARTINS T C, TSUZUKI M S G.Simulated annealing with adaptive neighborhood:A case study in off-line robot path planning[J]. Expert Systems with Applications, 2011, 38(4):2951-2965. doi: 10.1016/j.eswa.2010.08.084 [17] 薛永生, 吴立尧.基于模拟退火的改进粒子群算法研究及应用[J].海军航空工程学院学报, 2018, 33(2):248-252. http://d.old.wanfangdata.com.cn/Periodical/hjhkgcxyxb201802013XUE Y S, WU L Y.Research and application of improved PSO algorithm based on simulated annealing[J]. Journal of Naval Aeronautical and Astronautical University, 2018, 33(2):248-252(in Chinese). http://d.old.wanfangdata.com.cn/Periodical/hjhkgcxyxb201802013 -
下载:
点击查看大图
计量
- 文章访问数: 855
- HTML全文浏览量: 70
- PDF下载量: 440
- 被引次数: 0
