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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
  • [1] Ren W, Beard R W. Consensus seeking in multi-agent systems under dynamically changing interaction topologies. IEEE Transactions on Automatic Control, 2005, 50(5): 655−661 doi: 10.1109/TAC.2005.846556
    [2] Stankovic S S, Stankovic M S, Stipanovic D M. Consensus based overlapping decentralized estimator. IEEE Transactions on Automatic Control, 2009, 54(2): 410−415 doi: 10.1109/TAC.2008.2009583
    [3] 赵国荣, 韩旭, 卢建华. 一种基于数据驱动传输策略的带宽受限的分布式融合估计器. 自动化学报, 2015, 41(9): 1649−1658

    Zhao Guo-Rong, Han Xu, LU Jian-Hua. A decentralized fusion estimator using data-driven communication strategy subject to bandwidth constraints. Acta Automatica Sinica, 2015, 41(9): 1649−1658
    [4] 张勇刚, 王程程, 魏野, 李宁, 周卫东. 一种空间分布式变阶数自适应网络滤波算法. 自动化学报, 2014, 40(7): 1355−1365

    Zhang Yong-Gang, Wang Cheng-Cheng, Wei Ye, Li Ning, Zhou Wei-Dong. A spatially distributed variable taplength strategy over adaptive networks. Acta Automatica Sinica, 2014, 40(7): 1355−1365
    [5] Cavalcante R L G, Mulgrew B. Adaptive filter algorithms for accelerated discrete-time consensus. IEEE Transactions on Signal Processing, 2010, 58(3): 1049−1058 doi: 10.1109/TSP.2009.2032450
    [6] Liu W F, Tao D C, Cheng J, Tang Y. Multiview Hessian discriminative sparse coding for image annotation. Computer Vision & Image Understanding, 2014, 118: 50−60
    [7] Wang Y, Li, X R. A fast and fault-tolerant convex combination fusion algorithm under unknown cross-correlation. In: Proceeding of the 12th International Conference on Information Fusion. Seattle, USA: IEEE, 2009. 571–578
    [8] Xu C, Tao D C, Xu C. Multi-view intact space learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(12): 2531−2544 doi: 10.1109/TPAMI.2015.2417578
    [9] Deng Z L, Gao Y, Li C B. Self-tuning decoupled information fusion Wiener state component filters and their convergence. Automatica, 2008, 44: 685−695 doi: 10.1016/j.automatica.2007.07.008
    [10] Ran C J, Tao G L, Liu J F. Self-tuning decoupled fusion Kalman predictor and its convergence analysis. IEEE Sensors Journal, 2009, 9: 2024−2032 doi: 10.1109/JSEN.2009.2033260
    [11] Cong J L, Li Y Y, Qi G Q, et al. An Order Insensitive Sequential Fast Covariance Intersection Fusion Algorithm. Information Sciences, 2016, 367: 28−40
    [12] Julier S J, Uhlman J K. Non-divergent estimation algorithm in the presence of unknown correlations, In: Proceedings of the 1997 IEEE American Control Conference. Albuquerque, USA: IEEE, 1997. 2369–2373
    [13] Julier S J, Uhlman J K. Handbook of multisensor data fusion. Boca Raton: CRC Press, 2009. 196–283
    [14] Chen L J, Arambel P O, Mehra R K. Estimation under unknown correlation: covariance intersection revisited. IEEE Transactions on Automatic Control, 2002, 47: 1879−1882 doi: 10.1109/TAC.2002.804475
    [15] Reinhardt M, Noack B, Arambel P O, Hanebeck U D. Minimum Covariance Bounds for the Fusion under Unknown Correlations. IEEE Signal Processing Letters, 2015, 22(9): 1210−1214 doi: 10.1109/LSP.2015.2390417
    [16] Niehsen W, Gmbh R B. Information fusion based on fast covariance intersection filtering. In: Proceedings of the 5th International Conference on Information Fusion, Annapolis, USA: IEEE, 2002. 901–904
    [17] Franken D, Hupper A. Improved fast covariance intersection for distributed data fusion. In: Proceedings of the 8th International Conference on Information Fusion, Philadelphia, USA: IEEE, 2005. 1–7
    [18] Deng Z L, Zhang P, Qi W J, Gao Y. Sequential covariance intersection fusion Kalman filter. Information Sciences, 2012, 189: 293−309 doi: 10.1016/j.ins.2011.11.038
    [19] Tan H L, Shen B, Liu Y R, Alsaedi A, Ahmad B. Eventtriggered multi-rate fusion estimation for uncertain system with stochastic nonlinearities and colored measurement noises. Information Fusion, 2017, 36: 313−320 doi: 10.1016/j.inffus.2016.12.003
    [20] Wang X M, Liu W Q, Deng Z L. Robust weighted fusion Kalman estimators for multi-model multisensor systems with uncertain-variance multiplicative and linearly correlated additive white noises. Signal Processing, 2017, 137: 339−355 doi: 10.1016/j.sigpro.2017.02.015
    [21] Wang G Q, Li N, Zhang Y G. Diffusion distributed Kalman filter over sensor networks without exchanging raw measurements. Signal Processing, 2017, 132: 1−7 doi: 10.1016/j.sigpro.2016.07.033
    [22] Wang Y, Li X R. Distributed estimation fusion with unavailable cross-correlation. IEEE Transition on Aerospace Electronic Systems, 2012, 48(1): 259−278 doi: 10.1109/TAES.2012.6129634
    [23] 盛安冬, 王远钢. 满意率波在航迹辨识中的应用. 自动化学报, 2002, 28(4): 559−564

    Sheng An-Dong, Wang Yuan-Gang. Application of satisfactory filtering to tracking-identification problem. Acta Automatica Sinica, 2002, 28(4): 559−564
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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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