多传感器时滞系统CI融合滤波算法
|
摘要
针对多传感器信息融合时存在的时滞问题,建立了带状态和观测时滞的传感器网络移动目标追踪仿真模型,利用增广矩阵将时滞系统化为非时滞系统,提出了多传感器时滞系统协方差交叉(covariance intersection,CI)融合滤波算法。该方法避免了计算任意2个局部滤波误差互协方差阵,极大地减小了计算量与计算时间。分析了该方法的精度,比较了CI融合滤波算法与局部和最优融合Kalman滤波算法的精度,结果表明,CI融合滤波算法的精度高于局部滤波精度,但低于最优加权融合滤波精度。
In order to solve the problem of time delay in multi-sensor information fusion,a simulation model of moving target tracking in sensor networks with state and observation delay is established. Transforming time-delay systems into non-time-delay systems by using augmented matrices,a covariance intersection(CI) fusion filtering algorithm for multi-sensor systems with time-delay is presented in this paper. The method avoids the calculation of the mutual covariance matrix of any two local filtering errors,and the calculation amount and the calculation time are greatly reduced. The precision of the method is analyzed,and the accuracy of the covariance intersection fusion filtering algorithm and that of the local and optimal fusion Kalman filtering algorithm are compared. The results show that the accuracy of CI fusion filtering algorithm is higher than that of local filtering,but lower than that of optimal weighted fusion filtering.
In order to solve the problem of time delay in multi-sensor information fusion,a simulation model of moving target tracking in sensor networks with state and observation delay is established. Transforming time-delay systems into non-time-delay systems by using augmented matrices,a covariance intersection(CI) fusion filtering algorithm for multi-sensor systems with time-delay is presented in this paper. The method avoids the calculation of the mutual covariance matrix of any two local filtering errors,and the calculation amount and the calculation time are greatly reduced. The precision of the method is analyzed,and the accuracy of the covariance intersection fusion filtering algorithm and that of the local and optimal fusion Kalman filtering algorithm are compared. The results show that the accuracy of CI fusion filtering algorithm is higher than that of local filtering,but lower than that of optimal weighted fusion filtering.
引文
[1] Julier S J,Uhlmann J K. A non-divergent estimation algorithm in the presence of unknown correlations[C]. Proceedings of the American Control Conference,1997:264-268.
[2] Julier S,Uhlmann J K. General decentralized data fusion with covariance intersection in:Handbook of multi-sensor data fusion theory and practice[M]. Boca Raton:CRC Press,2009,21(2):19-342.
[3]何友,王国宏,陆大金.多传感器信息融合及应用[M].北京:电子工业出版社,2000:167-192.
[4]韩崇昭,朱红艳,段战胜,等.多源信息融合[M].北京:清华大学出版社,2006:158-201.
[5] Sun S L,Deng Z L. Multi-sensor optical information fusion Kalman filter[J].Automatic,2004,40(3):1017-1023.
[6]孙书利,崔平远.多传感器标量加权最优信息融合稳态Kalman滤波器[J].控制与决策,2004,19(2):208-211.
[7]邓自立.信息融合滤波理论及其应用[M].哈尔滨:哈尔滨工业大学出版社,2007:108-132.
[8]张鹏,齐文娟,邓自立,等.协方差交叉融合鲁棒Kalman滤波器[J].控制与决策,2012,27(6):904-908.
[9]马静,孙书利.随机奇异系统分布式最优分量融合滤波器[J].黑龙江大学学报,2007,24(4):508-512.
[10]邓自立,齐文娟,张鹏.鲁棒融合卡尔曼滤波理论及应用[M].哈尔滨:哈尔滨工业大学出版社,2016:35-83.
[11]邓自立,王欣,高媛.建模与估计[M].北京:科学出版社,2007:143-169.
[12]李艳辉,周秀杰,刘俊丽.基于T-S模糊模型的不确定时滞系统鲁棒L1滤波[J].控制与决策,2016,31(5):895~900.
[13]王光辉,孙书利.基于CI算法的多传感器时滞航迹的分布式融合估计[J].黑龙江大学工程学报,2015(3):68-72.
[14] Deng Z L,Gao Y,Mao L,et al.New approach to information fusion steady-state Kalman filtering[J].Automatica,2005,41(10):1695-1707.
[15]金学波,杜晶晶,鲍佳.基于伪测量的分布式最优单步延迟航迹融合估计[J].控制理论与应用,2011,28(10):1451-1454.
[16] Tao G L,Deng Z L. Self-tuning information fusion Kalman filter for multisensor multichannel ARMA signals with colored measurement noises and its convergence[J].Applied Mathematics&Information Sciences,2012,6(3):607-618.
[17] Chen B,Hu G Q,Ho D W C,et al.Distributed robust fusion estimation with application to state monitoring systems[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,47(11):2994-3005.
[18] Geng H,Liang Y,Liu Y,et al.Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs[J]. Information Fusion,2017,39:139-153.
[2] Julier S,Uhlmann J K. General decentralized data fusion with covariance intersection in:Handbook of multi-sensor data fusion theory and practice[M]. Boca Raton:CRC Press,2009,21(2):19-342.
[3]何友,王国宏,陆大金.多传感器信息融合及应用[M].北京:电子工业出版社,2000:167-192.
[4]韩崇昭,朱红艳,段战胜,等.多源信息融合[M].北京:清华大学出版社,2006:158-201.
[5] Sun S L,Deng Z L. Multi-sensor optical information fusion Kalman filter[J].Automatic,2004,40(3):1017-1023.
[6]孙书利,崔平远.多传感器标量加权最优信息融合稳态Kalman滤波器[J].控制与决策,2004,19(2):208-211.
[7]邓自立.信息融合滤波理论及其应用[M].哈尔滨:哈尔滨工业大学出版社,2007:108-132.
[8]张鹏,齐文娟,邓自立,等.协方差交叉融合鲁棒Kalman滤波器[J].控制与决策,2012,27(6):904-908.
[9]马静,孙书利.随机奇异系统分布式最优分量融合滤波器[J].黑龙江大学学报,2007,24(4):508-512.
[10]邓自立,齐文娟,张鹏.鲁棒融合卡尔曼滤波理论及应用[M].哈尔滨:哈尔滨工业大学出版社,2016:35-83.
[11]邓自立,王欣,高媛.建模与估计[M].北京:科学出版社,2007:143-169.
[12]李艳辉,周秀杰,刘俊丽.基于T-S模糊模型的不确定时滞系统鲁棒L1滤波[J].控制与决策,2016,31(5):895~900.
[13]王光辉,孙书利.基于CI算法的多传感器时滞航迹的分布式融合估计[J].黑龙江大学工程学报,2015(3):68-72.
[14] Deng Z L,Gao Y,Mao L,et al.New approach to information fusion steady-state Kalman filtering[J].Automatica,2005,41(10):1695-1707.
[15]金学波,杜晶晶,鲍佳.基于伪测量的分布式最优单步延迟航迹融合估计[J].控制理论与应用,2011,28(10):1451-1454.
[16] Tao G L,Deng Z L. Self-tuning information fusion Kalman filter for multisensor multichannel ARMA signals with colored measurement noises and its convergence[J].Applied Mathematics&Information Sciences,2012,6(3):607-618.
[17] Chen B,Hu G Q,Ho D W C,et al.Distributed robust fusion estimation with application to state monitoring systems[J].IEEE Transactions on Systems,Man,and Cybernetics:Systems,2017,47(11):2994-3005.
[18] Geng H,Liang Y,Liu Y,et al.Bias estimation for asynchronous multi-rate multi-sensor fusion with unknown inputs[J]. Information Fusion,2017,39:139-153.