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快速协方差交叉融合算法及应用

从金亮 李银伢 戚国庆 盛安冬

从金亮, 李银伢, 戚国庆, 盛安冬. 快速协方差交叉融合算法及应用. 自动化学报, 2020, 46(7): 1433−1444 doi: 10.16383/j.aas.c170410
引用本文: 从金亮, 李银伢, 戚国庆, 盛安冬. 快速协方差交叉融合算法及应用. 自动化学报, 2020, 46(7): 1433−1444 doi: 10.16383/j.aas.c170410
Cong Jin-Liang, Li Yin-Ya, Qi Guo-Qing, Sheng An-Dong. A fast covariance intersection fusion algorithm and its application. Acta Automatica Sinica, 2020, 46(7): 1433−1444 doi: 10.16383/j.aas.c170410
Citation: Cong Jin-Liang, Li Yin-Ya, Qi Guo-Qing, Sheng An-Dong. A fast covariance intersection fusion algorithm and its application. Acta Automatica Sinica, 2020, 46(7): 1433−1444 doi: 10.16383/j.aas.c170410

快速协方差交叉融合算法及应用

doi: 10.16383/j.aas.c170410
基金项目: 国家自然科学基金(61871221, 61876024),国防基础研究项目(JCKY2018209B010),江苏省高等学校自然科学研究项目(19KJB510015)资助
详细信息
    作者简介:

    从金亮:常熟理工学院电气与自动化工程学院讲师, 南京理工大学自动化学院博士研究生. 主要研究方向为分布式信息融合与目标跟踪. E-mail: congjinliang@njust.edu.cn

    李银伢:南京理工大学自动化学院副研究员. 主要研究方向为非线性估计理论及应用. E-mail: liyinya@njust.edu.cn

    戚国庆:南京理工大学自动化学院副研究员. 主要研究方向为随机状态估计, 多传感器数据融合. E-mail: qiguoqing@njust.edu.cn

    盛安冬:南京理工大学自动化学院研究员. 主要研究方向为多源信息融合, 非线性估计理论及应用. 本文通信作者. E-mail: shengandong@njust.edu.cn

A Fast Covariance Intersection Fusion Algorithm and Its Application

Funds: National Natural Science Foundation of China (61871221, 61876024),National Defense Basic Research Project of China (JCKY2018209B010), and Natural Science Research Projects of Colleges and Universities in Jiangsu Province (19KJB510015)
  • 摘要:

    针对分布式传感网络系统中存在互协方差未知的情形, 融合系数的科学设计对于融合性能至关重要. 本文以各节点估计方差矩阵逆的迹的倒数作为计算融合系数的中间变量, 设计了一种序贯快速协方差交叉融合算法, 可以显著减少各个融合节点的计算量, 能够保证各融合节点融合结果相同. 在给定系统的误差方差上界约束与优化指标前提下, 该融合算法结合粒子群优化算法, 能够给出对分布式系统中各个节点的传感器精度要求. 工程实践中, 可为传感器的选型提供理论依据. 最后, 给出了一个分布式网络传感器精度选型的算例及快速协方差交叉融合算法在雷达网中的应用实例.

  • 图  1  雷达系统配置图

    Fig.  1  Radar system configuration diagram

    图  2  各雷达跟踪轨迹及SFCI融合轨迹

    Fig.  2  Four radar tracking trajectories and SFCI fusion trajectory

    图  3  各雷达跟踪轨迹及SFCI融合轨迹水平投影

    Fig.  3  The horizontal projection of four radar tracking trajectories and SFCI fusion trajectory

    图  4  各雷达独立估计值及SFCI融合值距离分量误差

    Fig.  4  The errors of each radar estimate value and SFCI fusion value

    图  5  粒子群优化参数$\omega$$c_1+c_2$与优化成功率

    Fig.  5  The relationship of $\omega$$c_1+c_2$ and optimization success rate

    图  6  系统中各子节点估计值的$\rm MSE$与快速协方差交叉融合值的$\rm MSE$

    Fig.  6  The MSE of each local node estimation and fast covariance intersection fusion estimation

    图  8  系统中各子节点估计值速度分量的$\rm RMSE$与快速协方差交叉融合值速度分量的$\rm RMSE$

    Fig.  8  The velocity RMSE of each local node estimation and fast covariance intersection fusion estimation

    图  7  系统中各子节点估计值位置分量的$\rm RMSE$与快速协方差交叉融合值位置分量的$\rm RMSE$

    Fig.  7  The position RMSE of each local node estimation and fast covariance intersection fusion estimation

    图  9  系统中各子节点估计方差的迹与快速协方差交叉融合方差的迹

    Fig.  9  The trace of each local node estimation variance and fast covariance intersection fusion estimation variance

    图  10  本文所提算法与文献[18]对比结果

    Fig.  10  The comparison of SFCI with SCI in [18]

    图  11  图10的局部放大图

    Fig.  11  Partial enlarged view of Fig. 10

    图  12  图10的局部放大图

    Fig.  12  Partial enlarged view of Fig. 10

    表  1  本文与文献[18]算法复杂度对比

    Table  1  Comparison of algorithm complexity with [18]

    批处理计算复杂度序贯处理计算复杂度
    本文算法${\rm O}\left(n^3N\right)$${\rm O}\left(n^3N\right)$
    文献[18]算法${\rm O}\left(n^{3.5}{\rm{lg} }\left(\dfrac{n}{\varepsilon}\right)N^{2}\right)$${\rm O}\left(n^{3.5}{\rm{lg} }\left(\dfrac{n}{\varepsilon}\right)N\right)$
    下载: 导出CSV

    表  2  各节点平均单次融合耗时

    Table  2  Comparison of mean elapsed time in one period

    节点 1节点 2节点 3
    SCI 平均耗时 (ms)91.2089.5189.96
    SFCI 平均耗时 (ms) 0.61 0.59 0.63
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-07-24
  • 录用日期:  2018-05-07
  • 刊出日期:  2020-07-24

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