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PSO选星算法参数分析与改进

王尔申 杨迪 王传云 曲萍萍 庞涛 蓝晓宇

王尔申, 杨迪, 王传云, 等 . PSO选星算法参数分析与改进[J]. 北京航空航天大学学报, 2019, 45(11): 2133-2138. doi: 10.13700/j.bh.1001-5965.2019.0138
引用本文: 王尔申, 杨迪, 王传云, 等 . PSO选星算法参数分析与改进[J]. 北京航空航天大学学报, 2019, 45(11): 2133-2138. doi: 10.13700/j.bh.1001-5965.2019.0138
WANG Ershen, YANG Di, WANG Chuanyun, et al. Parameter analysis and improvement of PSO satellite selection algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2133-2138. doi: 10.13700/j.bh.1001-5965.2019.0138(in Chinese)
Citation: WANG Ershen, YANG Di, WANG Chuanyun, et al. Parameter analysis and improvement of PSO satellite selection algorithm[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(11): 2133-2138. doi: 10.13700/j.bh.1001-5965.2019.0138(in Chinese)

PSO选星算法参数分析与改进

doi: 10.13700/j.bh.1001-5965.2019.0138
基金项目: 

国家自然科学基金 61571309

国家自然科学基金 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

辽宁省教育厅科研项目 L201716

辽宁省高等学校优秀人才支持计划 LR2016069

详细信息
    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

    通讯作者:

    王尔申.E-mail:wanges_2016@126.com

  • 中图分类号: V241.6;TN967.1

Parameter analysis and improvement of PSO satellite selection algorithm

Funds: 

National Natural Science Foundation of China 61571309

National Natural Science Foundation of China 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

Scientific Study Project for Liaoning Province Department of Education L201716

Liaoning Excellent Talents in University LR2016069

More Information
  • 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。

     

  • 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
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出版历程
  • 收稿日期:  2019-04-01
  • 录用日期:  2019-05-31
  • 刊出日期:  2019-11-20

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