Comparative study on information fusion methods in constellation distributed autonomous orbit determination
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摘要:
为了保证大型导航星座在有限的星载运算能力和通信能力下,具备自主运行能力并提供精准位置参考信息,对基于分层结构的星座分布式自主定轨的信息融合方法展开了研究。以地月卫星联合星座作为研究对象,将简单凸组合法、协方差交叉融合法以及在线性最小方差意义下的矩阵加权法和标量加权法等方法应用于子滤波器估计的融合中,对各种融合方法的性能进行了对比分析。仿真结果显示,在采用方差放大技术去相关设计星座分布式自主定轨算法基础上,采用简单凸组合法、矩阵加权法和标量加权法3种融合方法的定轨精度较高,与集中式滤波精度相当,其中标量加权法的计算代价最低;而协方差交叉融合法由于难以准确确定最优系数,其精度低于其他3种方法。
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关键词:
- 多源信息融合 /
- 协方差交叉融合 /
- 线性最优加权融合准则 /
- 卫星自主定轨 /
- 半分布式滤波
Abstract:In order to ensure that the large-scale navigation constellation has autonomous operation capability and accurate position reference information with limited on-board computing capability and communication capability, the information fusion method of constellation distributed autonomous orbit determination based on hierarchical constellation is studied. Taking the Earth-Moon satellite joint constellation as the research object, covariance convex, covariance intersection and matrix weighting method and scalar weighting method in the sense of linear minimum variance are used to achieve fusion estimation of all sub-filters in distributed filter structure. The performance of various fusion algorithms was compared and analyzed. The simulation results show that, based on the constellation distributed autonomous orbit determination algorithm designed by variance amplifying technique, the orbit determination precision is high when covariance convex and matrix weighting method and scalar weighting method in the sense of linear minimum varianceare used, and the precision is equivalent to the centralized filtering precision, while the precision will get down when covariance intersection fusion is adopted because the optimal coefficient cannot be accurately searched.
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表 1 分层结构的子滤波器构成
Table 1. Hierarchical sub-filter structure
子滤波器编号 MEO卫星编号 拉格朗日卫星编号 异轨测量卫星编号 1 1, 2, 3, 4 La,Lb 9, 17 2 3, 4, 5, 6 La, Lb 9, 17 3 5, 6, 7, 8 La, Lb 9, 17 4 7, 8, 1, 2 La, Lb 9, 17 5 9, 10, 11, 12 La, Lb 1, 17 6 11, 12, 13, 14 La, Lb 1, 17 7 13, 14, 15, 16 La, Lb 1, 17 8 15, 16, 9, 10 La, Lb 1, 17 9 17, 18, 19, 20 La, Lb 1, 9 10 19, 20, 21, 22 La, Lb 1, 9 11 21, 22, 23, 24 La, Lb 1, 9 12 23, 24, 17, 18 La, Lb 1, 9 表 2 多源融合方法精度对比
Table 2. Precision comparison of multi-source fusion algorithm
滤波结构 融合方法 位置误差/m 速度误差/(0.01 m·s-1) x y z Er vx vy vz Ev 集中式 集中式滤波 5.467 549 9.577 071 10.514 803 14.237 305 0.108 099 0.148 421 0.155 596 9 0.240 675 分层式(存在异轨) 简单凸组合法 6.679 416 8.705 221 9.154 606 14.289 937 0.101 462 0.120 860 0.142 344 0.212 517 协方差交叉融合法(黄金分割法) 8.109 820 10.348 313 10.741 766 16.977 51 0.116 675 0.146 716 0.165 420 0.250 047 协方差交叉融合法(斐波那契法) 10.018 536 11.017 363 11.941 112 19.087 784 0.144 170 0.155 703 0.192 832 0.286 727 矩阵加权法 6.679 956 8.705 432 9.154 200 14.290 057 0.101 451 0.120 858 0.142 267 0.212 459 标量加权法 6.773 704 8.934 194 9.415 614 14.640 925 0.102 883 0.121 013 0.143 245 0.213 888 分层式(无异轨) 简单凸组合法 8.312 577 11.529 023 11.207 713 18.100 557 0.116 279 0.156 434 0.176 578 0.263 006 协方差交叉融合法(黄金分割法) 9.591 488 12.674 602 11.871 365 19.838 636 0.130 831 0.173 818 0.189 265 0.288 358 协方差交叉融合法(斐波那契法) 10.910 934 12.642 538 13.451 177 21.443 33 0.152 248 0.177 840 0.216 311 0.318 742 矩阵加权法 8.312 468 11.529 004 11.207 722 18.100 5 0.116 279 0.156 543 0.176 580 0.263 006 标量加权法 8.493 928 11.724 645 11.414 971 18.436 803 0.116 203 0.156 241 0.178 031 0.263 837 -
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