网络化多传感器信息融合估计算法研究
|
摘要
多传感器信息融合是20世纪70年代以来由于军事、国防和高科技领域的迫切需要发展起来的一门新兴边缘学科,多传感器信息融合估计作为该领域的一个重要分支在军事和民用领域有着重要的应用价值。随着网络通讯技术的飞速发展,在多传感器融合系统中引入有线或无线通信网络作为信息传输的枢纽,从而构成了网络化的多传感器融合系统(Networked Multi-Sensor Fusion Systems, NMFSs)。与传统的多传感器融合系统相比,NMFSs以其布线少、成本低、易于扩展和维护等优点,应用范围和作用在不断扩大,目前是一个热点研究课题。
然而,通信网络的引入带来了许多新的问题,从而传统的融合估计方法不适用于NMFSs,因此迫切需要提出适用于网络环境的多传感器信息融合估计理论。在这样的背景下,本文基于射影理论、Lyapunov理论、矩阵分析理论和最优加权融合算法,研究了NMFSs中存在的两个重要问题：一是通信带宽和传感器能量受限问题；二是传感器量测的不确定性及随机时延与丢包问题。研究工作主要包括：
1.研究了在通信带宽约束下NMFSs的分布式信息融合估计问题。首先提出了一种有结构限定的维数压缩策略来满足有限的通信带宽,并基于最优加权融合估计算法,导出了带宽受限的有限时域分布式Kalman融合估计器；最后给出了一个简单有效的分量传输方案使得融合估计性能在某些条件下是最优的。另一方面,当NMFSs中的噪声是统计特性未知的能量有界信号时,采用对数量化策略将要传输的局部估计信号量化到有限水平以满足有限的通信带宽;然后利用H∞滤波理论和离散系统的有界实引理导出了保证分布式H∞融合估计性能最优的充要条件,并给出了带宽约束条件下的最优加权矩阵和量化参数。以上研究结果分别通过移动目标跟踪系统和数值例子进行了验证。
2.研究了在通信带宽和能量受限下NMFSs的分布式Kalman融合估计问题。为了满足有限的通信带宽,每个时刻最多只有局部估计信号的部分分量以随机形式发送到融合中心,每个传感器则间歇性地发送信息到融合中心以达到节能目的。基于最优加权融合估计算法,给出了带宽和能量受限情况下的分布式Kalman融合估计算法。由于所设计的融合估计器性能依赖于传输分量的选择概率,为此导出了一些传输分量的概率选择准则以保证融合估计器的均方差(Mean Square Error, MSE)有界或收敛。最后,通过移动口标跟踪系统验证了所提方法的有效性。
3.研究了在通信量约束下NMFSs的分布式混合H2/H∞融合估计问题,其中系统扰动由高斯白噪声与能量有界噪声共同描述。为了缩减每个时刻传感器与融合中心的通信量,提出了有结构限定的随机维数压缩与对数量化相结合的策略。借助于Lyapunov理论和混合H2/H∞滤波方法,导出了关于量化参数与传输分量选择概率的充分条件以保证分布式H2/H∞融合估计器的稳定性。而且,当每个局部估计分量的传输概率预先给定时,给出了在通信量约束下最优加权矩阵和量化参数的设计方法。最后,通过F404航空发动机模型验证了所提方法的有效性。
4.研究了NMFSs中存在随机参数扰动、传感器失效、随机观测时延与丢包的融合估计问题。利用新息方法和代数Riccati方程导出了一个与原系统维数相等的集中式鲁棒Kalman融合估计器。与增广方法相比,它可以降低融合中心的计算复杂度,从而满足系统的实时性要求。基于所设计的集中式融合估计算法,利用矩阵满秩分解方法导出了存在上述不确定性的NMFSs的鲁棒降维加权观测融合Kalman估计器,进一步地减轻融合中心的计算负担。而且,导出了一些依赖于时延发生概率和传感器失效率的充分条件以保证估计器的稳定性和最优性,并给出了稳态Kalman融合估计器。最后,通过数值例子验证了所提方法的有效性。
5.研究了NMFSs中存在测量数据丢失、随机传输时延与丢包的分布式Kalman融合估计问题。首先,提出了一个新的随机通信模型来描述NMFSs中的随机传输时延和丢包现象；然后利用线性最优加权融合算法,导出了一个递推的分布式Kalman融合估计器。根据矩阵分析理论导出了一些使得融合估计器的MSE有界或者收敛的充分条件,并给出了稳态分布式Kalman融合估计器。最后,通过移动目标跟踪系统验证了所提方法的有效性。
Multi-sensor information fusion, which has attracted people's great attention, is a ris-ing multi-discipline field started from the1970s with the urgent need of the development of military affairs, national defense, wars and high-tech. As one of important issues in in-formation fusion, the multi-sensor fusion estimation problem has been a focus of research because of their wide application in military and civilian fields. With the rapid development of network communication technology, communication network is introduced to connect the distributed sensors and FC, and this class of systems may be called networked multi-sensor fusion systems (NMFSs). Compared with classical multi-sensor fusion systems, the insertion of the communication network in NMFSs can offer many advantages such as flex-ible architectures, simpler installation, easier maintenance and low cost. Therefore, NMFSs have now been one of the hot research topics.
Although NMFSs have brought so many advantages, they also bring lots of new prob-lems and difficulties, thus the conventional fusion estimation approaches will not be ap-plicable to the NMFSs. In this case, an important and practical problem is how to design fusion estimation algorithm for the NMFSs. Under this background, based on the projec-tion theory, Lyapunov theory, matrix analysis theory and optimal weighted fusion algo-rithm, this paper is concerned with two important prolems existing in NMFSs:one is the communication bandwidth and sensor energy constraints, the other is the problem of un-certain measurement, random delay and packet dropouts. The main work is summarized as follows:
1. The distributed fusion estimation problem is investigated for a class of NMFSs with communication bandwidth constraints. On one hand, a dimensionality reduction strategy with structural limit is proposed to satisfy finite bandwidth, then based on the optimal es-timation fusion algorithm weighted by matrices, a recursively distributed Kalman fusion estimator is derived, and a simple suboptimal judgement criterion is proposed to determine a group of binary variables such that the mean square error (MSE) of the designed estimator is minimum at each time step. On the other hand, when the process and measurement noises in the NMFSs have unknown statistic characteristic but bounded energy, a group of finite-level logarithmic quantizers are introduced to describe the case of bandwidth constraints. By using H∞filtering theory and the discrete-time bounded real lemma, the necessary and sufficient condition is derived such that the performance of the distributed H∞fusion es-timator is optimal. Under this condition, the optimal weighted matrices and quantization parameters are given. The target tracking system and an illustrative example are given to demonstrate the effectiveness of the proposed methods.
2. The distributed Kalman fusion estimation problem is investigated for a class of NMFSs with bandwidth and energy constraints. To satisfy the finite communication band-width, at a particular time, only partial components of each local estimate are allowed to be transmitted to the FC in a random way, while each sensor intermittently sends information to the FC for reducing energy consumptions. Then a recursively distributed fusion Kalman estimator is derived in the linear minimum variance sense. Since the performance of the de-signed estimator is dependent on the selecting probability of each component, some criteria for the choice of probabilities are derived such that the MSEs of the designed estimators are bounded or convergent. The steady-state distributed fusion Kalman estimator is also given. Finally, the target tracking system is given to demonstrate the effectiveness of the proposed methods.
3. The distributed mixed H2/H∞fusion estimation problem is investigated for a class of NMFSs with limited communication capacity, where the system perturbations are mod-eled by white noise and bounded energy noise. The stochastic dimensionality reduction strategy with structural limit and logarithmic qunatization strategy are simultaneously taken into account for deducing the traffic between the distributed sensors and FC. By resorting to Lyapunov theory and the mixed H2/H∞filtering approach, a sufficient condition, which is dependent on the quantization parameters and the selecting probabilities of the trans-mitted component, is derived such that the distributed mixed H2/H∞fusion estimator is stable. For given the transmitting probabilities of the estimate component, the design ap-proach for the optimal weighted matrices and quantization parameters is presented under the communication capacity constraints. Finally, the F-404aircraft engine model is given to demonstrate the effectiveness of the proposed methods.
4. The fusion estimation problem is investigated for a class of NMFSs with sensor fail-ures, stochastic parameter uncertainties, random observation delays and packet dropouts. A novel model is proposed to describe the random observation delays and packet dropouts, and a robust optimal fusion estimator is designed by using the innovation analysis method and algebraic Riccati equation. The dimension of the designed estimator is the same as the original system, which helps reduce computation cost as compared with the augmentation method, thus the designed estimator can satisfy the real-time performance of the system. Moreover, robust reduced-dimension observation fusion Kalman estimators are proposed to further reduce the computation burden. Some sufficient conditions for stability and optimal-ity of the designed fusion estimators are given, and steady-state Kalman fusion estimator is also presented. Simulation results show the effectiveness of the proposed methods.
5. The distributed Kalman fusion estimation problem is investigated for a class of NMFSs with missing sensor measurements, random transmission delays and packet dropouts. A novel stochastic model is proposed to describe the transmission delays and packet dropouts, and an optimal distributed Kalman fusion estimator is designed based on the optimal fusion criterion weighted by matrices. Some sufficient conditions are derived such that the MSE of the designed estimator is bounded or convergent. Moreover, steady-state distributed Kalman fusion estimator is also presented. Finally, the target tracking system is given to demonstrate the effectiveness of the proposed methods.
然而,通信网络的引入带来了许多新的问题,从而传统的融合估计方法不适用于NMFSs,因此迫切需要提出适用于网络环境的多传感器信息融合估计理论。在这样的背景下,本文基于射影理论、Lyapunov理论、矩阵分析理论和最优加权融合算法,研究了NMFSs中存在的两个重要问题：一是通信带宽和传感器能量受限问题；二是传感器量测的不确定性及随机时延与丢包问题。研究工作主要包括：
1.研究了在通信带宽约束下NMFSs的分布式信息融合估计问题。首先提出了一种有结构限定的维数压缩策略来满足有限的通信带宽,并基于最优加权融合估计算法,导出了带宽受限的有限时域分布式Kalman融合估计器；最后给出了一个简单有效的分量传输方案使得融合估计性能在某些条件下是最优的。另一方面,当NMFSs中的噪声是统计特性未知的能量有界信号时,采用对数量化策略将要传输的局部估计信号量化到有限水平以满足有限的通信带宽;然后利用H∞滤波理论和离散系统的有界实引理导出了保证分布式H∞融合估计性能最优的充要条件,并给出了带宽约束条件下的最优加权矩阵和量化参数。以上研究结果分别通过移动目标跟踪系统和数值例子进行了验证。
2.研究了在通信带宽和能量受限下NMFSs的分布式Kalman融合估计问题。为了满足有限的通信带宽,每个时刻最多只有局部估计信号的部分分量以随机形式发送到融合中心,每个传感器则间歇性地发送信息到融合中心以达到节能目的。基于最优加权融合估计算法,给出了带宽和能量受限情况下的分布式Kalman融合估计算法。由于所设计的融合估计器性能依赖于传输分量的选择概率,为此导出了一些传输分量的概率选择准则以保证融合估计器的均方差(Mean Square Error, MSE)有界或收敛。最后,通过移动口标跟踪系统验证了所提方法的有效性。
3.研究了在通信量约束下NMFSs的分布式混合H2/H∞融合估计问题,其中系统扰动由高斯白噪声与能量有界噪声共同描述。为了缩减每个时刻传感器与融合中心的通信量,提出了有结构限定的随机维数压缩与对数量化相结合的策略。借助于Lyapunov理论和混合H2/H∞滤波方法,导出了关于量化参数与传输分量选择概率的充分条件以保证分布式H2/H∞融合估计器的稳定性。而且,当每个局部估计分量的传输概率预先给定时,给出了在通信量约束下最优加权矩阵和量化参数的设计方法。最后,通过F404航空发动机模型验证了所提方法的有效性。
4.研究了NMFSs中存在随机参数扰动、传感器失效、随机观测时延与丢包的融合估计问题。利用新息方法和代数Riccati方程导出了一个与原系统维数相等的集中式鲁棒Kalman融合估计器。与增广方法相比,它可以降低融合中心的计算复杂度,从而满足系统的实时性要求。基于所设计的集中式融合估计算法,利用矩阵满秩分解方法导出了存在上述不确定性的NMFSs的鲁棒降维加权观测融合Kalman估计器,进一步地减轻融合中心的计算负担。而且,导出了一些依赖于时延发生概率和传感器失效率的充分条件以保证估计器的稳定性和最优性,并给出了稳态Kalman融合估计器。最后,通过数值例子验证了所提方法的有效性。
5.研究了NMFSs中存在测量数据丢失、随机传输时延与丢包的分布式Kalman融合估计问题。首先,提出了一个新的随机通信模型来描述NMFSs中的随机传输时延和丢包现象；然后利用线性最优加权融合算法,导出了一个递推的分布式Kalman融合估计器。根据矩阵分析理论导出了一些使得融合估计器的MSE有界或者收敛的充分条件,并给出了稳态分布式Kalman融合估计器。最后,通过移动目标跟踪系统验证了所提方法的有效性。
Multi-sensor information fusion, which has attracted people's great attention, is a ris-ing multi-discipline field started from the1970s with the urgent need of the development of military affairs, national defense, wars and high-tech. As one of important issues in in-formation fusion, the multi-sensor fusion estimation problem has been a focus of research because of their wide application in military and civilian fields. With the rapid development of network communication technology, communication network is introduced to connect the distributed sensors and FC, and this class of systems may be called networked multi-sensor fusion systems (NMFSs). Compared with classical multi-sensor fusion systems, the insertion of the communication network in NMFSs can offer many advantages such as flex-ible architectures, simpler installation, easier maintenance and low cost. Therefore, NMFSs have now been one of the hot research topics.
Although NMFSs have brought so many advantages, they also bring lots of new prob-lems and difficulties, thus the conventional fusion estimation approaches will not be ap-plicable to the NMFSs. In this case, an important and practical problem is how to design fusion estimation algorithm for the NMFSs. Under this background, based on the projec-tion theory, Lyapunov theory, matrix analysis theory and optimal weighted fusion algo-rithm, this paper is concerned with two important prolems existing in NMFSs:one is the communication bandwidth and sensor energy constraints, the other is the problem of un-certain measurement, random delay and packet dropouts. The main work is summarized as follows:
1. The distributed fusion estimation problem is investigated for a class of NMFSs with communication bandwidth constraints. On one hand, a dimensionality reduction strategy with structural limit is proposed to satisfy finite bandwidth, then based on the optimal es-timation fusion algorithm weighted by matrices, a recursively distributed Kalman fusion estimator is derived, and a simple suboptimal judgement criterion is proposed to determine a group of binary variables such that the mean square error (MSE) of the designed estimator is minimum at each time step. On the other hand, when the process and measurement noises in the NMFSs have unknown statistic characteristic but bounded energy, a group of finite-level logarithmic quantizers are introduced to describe the case of bandwidth constraints. By using H∞filtering theory and the discrete-time bounded real lemma, the necessary and sufficient condition is derived such that the performance of the distributed H∞fusion es-timator is optimal. Under this condition, the optimal weighted matrices and quantization parameters are given. The target tracking system and an illustrative example are given to demonstrate the effectiveness of the proposed methods.
2. The distributed Kalman fusion estimation problem is investigated for a class of NMFSs with bandwidth and energy constraints. To satisfy the finite communication band-width, at a particular time, only partial components of each local estimate are allowed to be transmitted to the FC in a random way, while each sensor intermittently sends information to the FC for reducing energy consumptions. Then a recursively distributed fusion Kalman estimator is derived in the linear minimum variance sense. Since the performance of the de-signed estimator is dependent on the selecting probability of each component, some criteria for the choice of probabilities are derived such that the MSEs of the designed estimators are bounded or convergent. The steady-state distributed fusion Kalman estimator is also given. Finally, the target tracking system is given to demonstrate the effectiveness of the proposed methods.
3. The distributed mixed H2/H∞fusion estimation problem is investigated for a class of NMFSs with limited communication capacity, where the system perturbations are mod-eled by white noise and bounded energy noise. The stochastic dimensionality reduction strategy with structural limit and logarithmic qunatization strategy are simultaneously taken into account for deducing the traffic between the distributed sensors and FC. By resorting to Lyapunov theory and the mixed H2/H∞filtering approach, a sufficient condition, which is dependent on the quantization parameters and the selecting probabilities of the trans-mitted component, is derived such that the distributed mixed H2/H∞fusion estimator is stable. For given the transmitting probabilities of the estimate component, the design ap-proach for the optimal weighted matrices and quantization parameters is presented under the communication capacity constraints. Finally, the F-404aircraft engine model is given to demonstrate the effectiveness of the proposed methods.
4. The fusion estimation problem is investigated for a class of NMFSs with sensor fail-ures, stochastic parameter uncertainties, random observation delays and packet dropouts. A novel model is proposed to describe the random observation delays and packet dropouts, and a robust optimal fusion estimator is designed by using the innovation analysis method and algebraic Riccati equation. The dimension of the designed estimator is the same as the original system, which helps reduce computation cost as compared with the augmentation method, thus the designed estimator can satisfy the real-time performance of the system. Moreover, robust reduced-dimension observation fusion Kalman estimators are proposed to further reduce the computation burden. Some sufficient conditions for stability and optimal-ity of the designed fusion estimators are given, and steady-state Kalman fusion estimator is also presented. Simulation results show the effectiveness of the proposed methods.
5. The distributed Kalman fusion estimation problem is investigated for a class of NMFSs with missing sensor measurements, random transmission delays and packet dropouts. A novel stochastic model is proposed to describe the transmission delays and packet dropouts, and an optimal distributed Kalman fusion estimator is designed based on the optimal fusion criterion weighted by matrices. Some sufficient conditions are derived such that the MSE of the designed estimator is bounded or convergent. Moreover, steady-state distributed Kalman fusion estimator is also presented. Finally, the target tracking system is given to demonstrate the effectiveness of the proposed methods.
引文
[1]D.L. Hall, J. Llinas. An introduction to multi-sensor data fusion [J]. Proceedings of The IEEE, 1987,85(1):6-23.
[2]Y. Bar-Shalom, X.R. Li, T. Kirubarajan. Estimation with Applications to Tracking and Navigation [M]. John Wilely and Sons, Inc,2001.
[3]Y. Zhu. Multisensor decision and estimation fusion [M]. Kluwer academic publishers,2002.
[4]潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展[J].自动化学报,2003,29(4)：599-644.
[5]杨露菁,余华.多源信息融合理论与应用[M].北京：北京邮电大学出版社,2005.
[6]韩崇昭,朱洪艳,段战胜.多源信息融合[M].北京：清华大学出版社,2006.
[7]彭冬亮,文成林,薛安克.多传感器多源信息融合理论及应用[M].北京：科学出版社.2010.
[8]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.
[9]P. Kumar, A. Mittal. Addressing uncertainty in multi-modal fusion for improved object detection in dynamic environment [J].Information Fusion,2010,11(4):311-324.
[10]B. Khaleghi, A. Khamis, F. O.Karray, S. N.Razzavi. Multisensor data fusion:A review of the state-of-the art [J].Information Fusion,2013,14(1):28-44.
[11]张高煜.多传感器多源信息融合理论及应用[D].西安电子科技大学,2006.
[12]Y. Xia, M. Fu, G. Liu. Analyais and Synthesis of Netwoked Control Systems [M]. New York: Springer,2011.
[13]Y. Zhu, J. Zhou, X. Shen, E. Song et al. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods [M]. CRC Press,2012.
[14]潘泉,王增福,梁彦,杨峰,刘准钆.信息融合理论的基本方法与进展(Ⅱ)[J].控制理论与应用,2012,29(10)：1233-1244.
[15]B. Chen, Li Yu, W.A. Zhang, H. Song. Netorked Multi-Sensor Fusion Estimation with Delays, Packet Losses and Missing Measurements [A].12th International Conference on Control, Au-tomation, Robotics and Vision [C]. Guangzhou, China,2012:695-700.
[16]E.F. Nakamura. Information Fusion for Wireless Sensor Networks:Methods, Models, and Clas-sifications [J]. ACM Computing Surveys,2007,39(3):Article 9.
[17]张瑞华.基于能量效率的无线传感器网络关键技术研究[D].山东大学,2007.
[18]A. Oka, L. Lampe. Energy Efficient Distributed Filtering with Wireless Sensor Networks [J]. IEEE Transactions on Signal Processing,2008,56(5):2062-2075.
[19]J.P. Hespanha, P. Naghshtabrizi, Y. G. Xu. A survey of recent results in networked control systems [J]. Proceedings of The IEEE,2007,95(1):138-161.
[20]W.P.M. H.Heemels, A. R.Teel, N. Wouw, et al. Networked Control Systems with Communica-tion Constraints:Tradeoffs Between Transmission Intervals, Delays and Performance [J]. IEEE Transactions on Automatic Control,2010,55(8):1781-1796.
[21]张文安.网络化控制系统的时延与丢包问题研究[D].浙江工业大学,2010.
[22]W.S. Song, R.W. Brockett. System with finite communication bandwidth constraints-I:state es-timation problems [J]. IEEE Transactions on Automatic Control,1997,42(9):1294-1299.
[23]V. Malyavej and A. V. Savkin. The Problem of optimal robust Kalman state estimation via limited capacity digital communication channels [J]. Systems and Control Letters,2005,54(3):283-292.
[24]M. Sahebsara, T. Chen, S.L. Shah. Optimal H2 filtering with random sensor delay, multiple packet dropouts and uncertain observations [J]. International Journal of Control,2007,80:292-301.
[25]王宝凤.网络化控制系统中状态估计问题的研究[D].大连海事大学,2010.
[26]J. Ma, S. Sun. Optimal linear estimators for systems with random sensor delays, multiple packet dropouts and uncertain observations [J]. IEEE Transactions on Signal Processing,2011,59: 5181-5192.
[27]H. Zhang, G. Feng, C. Han. Linear estimation for random delay systems [J]. System Control Letters,2011,60(7):450-459.
[28]马静.网络化控制系统的建模与融合估计[D].黑龙江大学,2012.
[29]J.J. Xiao, A. Ribeiro, Z.Q. Luo, G.B. Giannakis. Distributed compression estimation using wire-less sensor networks [J]. IEEE Signal Processing Magazine,2006,7:27-41.
[30]A. Ribeiro, I.D. Schizas, S.I. Roumeliotis, G.B. Giannakis. Kalman filtering in wireless sensor networks [J]. IEEE Control System Magazine,2010,4:66-86.
[31]R. Singer, A.J. Kanyuck. Computer control of multiple site track correlation [J]. Automation, 1971,7:455-464.
[32]D. Willner, C.B. Chang, D.A. Castanon, et al. Kalman filter algorithm for a multisensor system [A]. Proceedings of IEEE Conferences on Decision and Control including the 15th Symposium on Adaptive Processes [C].1976,15:570-574.
[33]M.F. Hassan, G. Salut, M.G. Singh et al. A decentralized computational algorithm for the global Kalman filter [J]. IEEE Transactions on Automatic Control,1978,23(2):262-268.
[34]C.Y. Chong. Hierarchical estimation [A]. Procceedings of the 2nd MIT/ONR Workshop on C3 systems [C]. Monterey, California,1979.
[35]X.R Li, Y. Zhu, J. Wang, C.Z. Han. Optimal linear estimation fusion-part Ⅰ:unified fusion rules [J]. IEEE Transactions on Information Theory,2003,49:2192-2208.
[36]X.R. Li, J. Wang. Unified optimal linear estimation fusion-Part Ⅱ:Discussions and examples [A]. Procceedings of International Conference on Information Fusion [C]. USA,2000:MoC2.18-MoC2.25.
[37]X.R. Li, P. Zhang. Optimal linear estimation fusion-part Ⅲ:Cross-correlation of local estimation errors [A]. Procceedings of International Conference on Information Fusion [C]. USA,2001: WeB1.11-WeB1.18.
[38]X.R. Li, K.S. Zhang. Optimal linear estimation fusion-part Ⅳ:Optimality and efficiency of dis-tributed fusion [A]. Procceedings of International Conference on Information Fusion [C]. USA, 2001:WeB1.19-WeB 1.26.
[39]Z. Duan, X.R. Li. Lossless Linear transformation of sensor data for distributed estimation fusion [J]. IEEE Transactions on Singal Processing,2011,59:362-372.
[40]Y. Gao, W.J. Jia, X.J. Sun, Z.L. Deng. Self-Tuning multisensor weighted measurement fusion Kalman filter [J]. IEEE Transactions on Aerospace and Electronic Systems,2009,45(1):179-191.
[41]Y. Gao, C. Ran, Z.L. Deng. Weighted measurement fusion Kalman filer based on linear unbiased minimum variance criterion [A]. Joint 48th IEEE Conference on Decesion and Control and 28th Chinese Control Conference [C]. Shanghai, China,2009:7593-7598.
[42]冉陈键.最优和自校正多传感器加权观测融合卡尔曼滤波器[D].黑龙江大学,2011.
[43]郝钢,叶秀芬,陈亭.加权观测融合非线性无迹卡尔曼滤波算法[J].控制理论与应用,2011,28(6)：753-758.
[44]Q. Gan, C.J. Harris. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion [J]. IEEE Transactions on Aerospace and Electronic Systems,2001,37: 273-280.
[45]邓自立.两种最优观测融合方法的功能等价性[J].控制理论与应用,2006,23(2)：319-323.
[46]H.M. Chen, T. Kirubarjan, Y. Bar-Shalom. Track-to-track fusion versus centralized estimation: Theory and application [J]. IEEE Transactions on Aerospace and Electronic Systems,2003, 39(2):386-411.
[47]Y. Bar-shalom, L. Capmo. The effect of the common process noise on the two-sensor fused track covariance [J]. IEEE Transactions on Aerospace and Electronic Systems,1986,22(6):803-805.
[48]Y. Bar-shalom. On the track-to-track correlation problem [J]. IEEE Transactions on Automatic Control,1981,26:571-572.
[49]G.J. Bierman, M.R. Belzer. A decentralized square root information filter/smoother [A]. Pro-ceedings of 24th IEEE Conference on Decision and Control [C]. Ft. Lauderdale, Fl, USA,1985: 1902-1905.
[50]S. Roy, R.H. Hashem, A.J. Laub. Square root filter for parallel Kalman filtering using reduced-order local filters [J]. IEEE Transactions on Aerospace and Electronic Systems,1991,27(2): 276-288.
[51]D.A. Castanon, D. Teneketzis. Distributed estimaiton algorithm for nonlinear system [J]. IEEE Transactions on Automatic Control,1985,30(5):418-425.
[52]N.A. Carlson. Federated square root filter for decentralized parallel processes[J]. IEEE Transac-tions on Aerospace and Electronic Systems,1990,26:517-525.
[53]K.H. Kim. Development of track to track fusion algorithm [A]. American Control Conference [C]. Maryland, America,1994:1037-1041.
[54]S.L. Sun, Z.L. Deng. Multi-sensor optimal information fusion Kalman filter [J]. Automatica, 2004,40:1017-1023.
[55]Z.L. Deng, y. Gao, L. Mao, et al. New approach to information fusion steady-state Kalman filter-ing [J]. Automatica,2005,41(10):1695-1707.
[56]S.L. Sun. Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother [J]. Automatica,2005,41(12):2153-2159.
[57]石莹.广义系统的状态估计[D].哈尔滨工业大学,2007.
[58]L. Chen, P.O. Arambel, R.K. Mehra. Estimation under unknown correlation:covariance intersec-tion revisited [J]. IEEE Transactions on Automatic Control,2002,47:1879-1882.
[59]Z.L. Deng, P. Zhang, W.J. Qi, et al. Sequential covariance intersection fusion Kalman filter [J]. Information Sciences,2012,189:293-309.
[60]金学波.多传感器状态融合估计理论与应用研究[D].浙江大学,2003.
[61]李庆华.H∞多传感器状态融合估计理论与应用研究[D].山东大学,2009.
[62]冯肖亮,文成林,刘伟峰等.基于多传感器的序贯式融合有限域H∞滤波方法[J].自动化学报,2013,39(9)：1523-1532.
[63]V. Shnayder, M. Hempstead, B. Chen, et al. Simulating the power consumption of large-scale sensor network applications [A]. SenSys [C].2004:188-200.
[64]J. A.Gubner. Distributed estimaiton and quantization [J]. IEEE Transactions on Information The-ory,1993,39(4):1456-1459.
[65]Z.Q. Luo. Universal decentralized estimation in a bandwidth constrained sensor networks [J]. IEEE Transactions on Information Theory,2005,51(6):2210-2219.
[66]A.Riberio, G. B.Giannakis. Bandwidth-constrained distributed estimation for wireless sensor networks-Part Ⅰ:Gaussian case [J]. IEEE Transactions on Signal Processing,2006,54:1131-1143.
[67]A. Riberio, G. B.Giannakis, S. I.Roumeliotis. SOI-KF:Distributed Kalman filtering with low-cost communication using the sign of innovations [J]. IEEE Transactions on Signal Processing,2006, 54:4782-4795.
[68]J. Fang, H. Li. Distributed adaptive quantization for wireless sensor networks:From delat modu-lation to maximum likelihood [J]. IEEE Transactions on Signal Processing,2008,56(10):5246-5247.
[69]S.L. Sun, J.Y. Lin, L.H. Xie, W.D. Xiao. Quantized Kalman filtering [A]. The 22nd IEEE Inter-national Symposium on Intelligent Control [C]. Singapore,2007:7-12.
[70]C.L. Wen, Q.B Ge, X.F. Tang. Kalman filtering in a bandwidth constrained sensor network [J]. Chinese Journal of Electronics,2009,18(4):713-718.
[71]X. Shen, Y. Zhu, Z. You. An efficient sensor quantization algorithm for decentralized estimation fusion [J]. Automatica,2011,47:1053-1059.
[72]陈军勇,邬依林,祁恬.无线传感器网络分布式量化卡尔曼滤波[J].控制理论与应用,2011,28(12)：1729-1739.
[73]J.J. Xiao, S. Cui, Z.Q. Luo, A. J.Goldsmith. Power scheduling of universal decentralized estima-tion in sensor networks [J]. IEEE Transactions on Signal Processing,2006,54(2):413-422.
[74]J. Li, G. AlRegib. Distributed estimation in energy-constrained wireless sensor networks [J]. IEEE Transactions on Signal Processing,2009,57(10):3746-3758.
[75]J. Fang, H. Li. Hyperplane-based vector quantization for distributed estimation in wireless sensor networks [J]. IEEE Transactions on Information Theory,2009,55(12):5682-5699.
[76]Z.Q. Luo, J. N.Tsitiklis. Data fusion with minimal communication [J]. IEEE Transactions on Information Theory,1994,40(5):1551-1563.
[77]Z.Q. Luo, J. N.Tsitiklis. Data fusion with minimal communication [J]. IEEE Transactions on Singal Processing,2005,53(5):1631-1639.
[78]宋恩彬.信息融合与处理中几个问题的进展[D].四川大学,2007.
[79]I. D.Schizas, G. B.Giannakis, Z.Q. Luo. Distributed estimation using reduced-dimensionality sen-sor observations [J]. IEEE Transactions on Singal Processing,2007,55(8):4284-4299.
[80]A. S.Behbahani, A. M.Eltawil, H. Jafarkhani. Linear decentralized estimation of correlated data for power-constrained wireless sensor networks [J]. IEEE Transactions on Singal Processing, 2012,60(11):6003-6016.
[81]J.J. Xiao, S. Cui, Z.Q. Luo, A. J.Goldsmith. Linear coherent decentralized estimation [J]. IEEE Transactions on Singal Processing,2008,56(2):757-770.
[82]J. Fang, H. Li. Joint dimension assignment and compression for distributed multisensor estimation [J]. IEEE Singal Processing Letters,2008,15:174-177.
[83]H. Zhu, I. D.Schizas, G. B.Giannakis. Power-efficient dimensionality reduction for distributed channel-aware Kalman tracking using WSNs [J]. IEEE Transactions on Singal Processing,2009, 57(8):3193-3207.
[84]H. Chen, X.R. Li. On track fusion with communicaiton constraints [A]. Proceedings of the 10th International Conference on Information Fusion [C].2007:9-12.
[85]W. Koch. On optimal distributed Kalman filtering and retrodiction at arbitary communication rates for maneuvering targets [A]. Proceedings of IEEE International Conferences on Multisensor Fusion and Integration for Intelligent Systems [C]. Seoul, Korea,2008:457-462.
[86]R. S.Blum. Ordering for estimation and optimization in energy efficient sensor networks[J]. IEEE Transactions on Singal Processing,2011,59(6):2847-2856.
[87]G. Battistelli, A. Benavoli, L. Chisci. State estimation with remote sensors and intermittent trans-missions [J]. Systems and Control Letters,2011,61:155-164.
[88]W.A. Zhang, Li Yu, X. Qiu, B. Chen. Energy-efficient fusion estimation for wireless sensor net-works with packet losses [A]. Proceedings of the 30th Chinese Control Conference [C]. Yantai, China,2011:6408-6412.
[89]W.A. Zhang, G. Feng, L. Yu. Multi-rate distributed fusion estimation for sensor networks with packet losses [J]. Automatica,2012,48:2016-2028.
[90]A. Chiuso, L. Schenato. Information fusion strategies and performance bounds in packet-drop networks [J]. Automatica,2011,47:1304-1316.
[91]Y. Liang, T. Chen, Q. Pan. Multi-rate stochastic H∞ filteing for networked multi-sensor fusion [J]. Automatica,2010,46:437-444.
[92]J. Ma, S. Sun, Information fusion estimators for systems with multiple sensors of different packet dropout rates [J]. Information Fusion,2011,12:213-222.
[93]R.K. Varshney, P.K. Varshney. Recursive estimation with uncertain observations in multisensor environment [J]. IEE Proceedings,1986,133:527-533.
[94]F. O.Hounkpevi, E. E.Yaz. Robust minimum variance linear state estimators for multiple sensors with different failure rates [J]. Automatica,2007,43:1274-1280.
[95]N. Lv, S.L. Sun. Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements [A]. Proceedings of the 48th IEEE Conference on Decision and Control [C]. Shanghai, China,2009:7599-7602.
[96]孙书利,吕楠.带有色观测噪声多传感器多重时滞系统分布式融合滤波器[J].自动化学报,2009,35(1)：46-53.
[97]J. Sun, C. Zhang, J. Gu. Decentralized optimal fusion filtering for multi-sensor multi-delay sin-gular systems [J]. Circuits Systems and Signal Processing,2012,31:163-176.
[98]X.J. Sun, Z.L. Deng. Information fusion wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements [J]. IET Signal Processing,2009,3:403-415.
[99]Y. Xia, J. Shang, J. Chen, G.P. Liu. Networked data fusion with packet losses and variable delays [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics,2009,39(5): 1107-1120.
[100]I. Penarrocha, R. Sanchis, P. Albertos. Estimation in multisensor networked systems with scarce measurements and time-varying delays [J]. Systems Control Letters,2012,61:555-562.
[101]H. Zhang, Y. Shi, A.S. Mehr. Robust weighted H∞ filtering for networked systems with intermit-tent measurements of multiple sensors [J]. International Jouranl of Adaptive Control and Signal Processing,2011,25:313-330.
[102]M. Liu, M. Qiu, S. Zhang, Z. Lin. Robust H∞ fusion filtering for discrete-time nonlinear delayed systems with missing measurement [A]. Proceedings of the 2010 American Control Conference [C].2010:6703-6808.
[103]M. Liu, D. Qi, S. Zhang, et al. Optimal H∞ fusion filters for a class of discrete-time intelligent systems with time delays and missing measurement [J]. Neurcomputing,2011,74:3741-3752.
[104]R. Rahman, M. Alanyali, V. Saligrama. Distributed tracking in multihop sensor networks with communication delays [J]. IEEE Transactions on Singal Processing,2007,55(9):4656-4668.
[105]L. Schenato. Optimal sensor fusion for distributed sensors subject to random delay and packet loss [A]. Proceedings of the 46th IEEE Conference on Decision and Control [C].2007:1547-1552.
[106]S.L. Sun, W. Xiao. Distributed weighted fusion estimation with random delays and packet drop-ping [J]. Circuits Systems and Signal Processing,2007,26(4):591-605.
[107]J. Ma, S. Sun. Centralized fusion estimators for multisensor systems with random sensor delays, multiple packet dropouts and uncertain observations [J]. IEEE Sensors Journal,2013,13(4): 1228-1235.
[108]R.A. Horn, C.R. Johnson. Topics in matrix analysis [M]. New York, Cambrideg University Press, 1991.
[109]F. Hu, Y. Wang, H. Wu. Mobile telemedicine sensor networks with low-energy data query and network lifetime considerations [J]. IEEE Transactions on Mobile Computing,2006,5(4):404-417.
[110]C.K. Chui, G. Chen. Kalman filtering with real-time applications [M]. Springer,1999.
[111]X.R. Li, V.P. Jilkov. Survey of maneuvering target tracking, part I:Dynamic models [J]. IEEE Transactions on Aerospace and Electronic Systems,2003,39:1333-1364.
[112]俞立.鲁棒控制-线性矩阵不等式处理方法[M].北京：清华大学出版社,2002.
[113]M. Fu, L. Xie. The sector bound appraoch to quantized feedback control [J]. IEEE Transactions on Automatic Control,2005,50:1698-1711.
[114]C.E. de Souza, L. Xie. On the discrete-time bounded real lemma with application in the charac-terization of static feedback H∞ controllers [J]. Systems Control Letters,1992,18:61-71.
[115]孙利民,李建中,陈渝,等.无线传感器网络[M].北京：清华大学出版社,2005.
[116]A.H. Jazwioski. Stochastic Processes and Filtering Theory [M]. New York:Academic,1970.
[117]S.D. Wang, T.S. Kuo, C.F. Hsu. Trace bounds on the solution of the algebraic matrix riccati and lyapunov equation [J]. IEEE Transactions on Automatic Control,1986,31:654-656.
[118]R. M.Palhares, P. L.D.Peres. Optimal filtering schemes for linear discrete-time systems:a linear matrix inequality approach [J]. International Jouranl of Systems Science,1998,29(6):587-593.
[119]R. M.Palhares, P. L.D.Peres. LMI approach to the mixed H2/H∞ filtering design for discrete-time uncertain systems [J]. IEEE Transactions on Aerospace and Electronic Systems,2001,37(1): 292-296.
[120]H. Gao,J. Lam, L. Xie, C.Wang. New approach to mixed H2/H∞ filtering for polytopic discrete-time systems [J]. IEEE Transactions on Signal Processing,2005,53(8):3183-3192.
[121]M. Sznaier. New exact solution to general SISO mixed H2/H∞ problems via convex optimization [J]. IEEE Transactions on Automatic Control,1994,39(12):2511-2517.
[122]R. W.Eustace, B. A.Woodyatt, G. L.Merrington, A. Runacres. Fault signatures obtained from fault implant tests on an F404 Engine [J]. Transactions of the ASME,1994,116:176-183.
[123]W.W. Che, G.H. Yang. Quantised H∞ filter design for discrete-time systems [J]. International Journal of Control,2009,82(2):195-206.
[124]J. Hu, Z. Wang, H. Gao, L. K.Stergioulas. Robust sliding mode control for discrete stochastic systems with mixed time delays, randomly occurring uncertainties, and randomly occurring non-linearties [J]. IEEE Transactions on Industrial Electronics,2012,59(7):3008-3015.
[125]J. Nilson. Real-time control systems with delays [D]. Lund Institute of Technology,1998.
[126]H. Gao, X. Meng, T. Chen. Stabilization of networked control systems with a new delay charac-terization [J]. IEEE Transactions on Automatic Control,2008,53:2142-2148.
[127]H. Dong, Z. Wang, H. Gao. Robust H∞ filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts [J]. IEEE Transactions on Signal Processing,2010,58:1957-1966.
[128]R. Piziak, P.L. Odell. Full rank factorization of matrices [J]. Mathematics Magazine,1999,72: 193-201.
[129]F. Wang, V. Balakrishnan. Robust Kalman filters for linear time-varying systems with stochastic parameter uncertainties[J]. IEEE Transactions on Signal Processing,2002,50:803-813.
[130]T. Kailath, A.H. Syayed, B. Hassibi. Linear Estimation [M]. Prentice Hall,2000.
[2]Y. Bar-Shalom, X.R. Li, T. Kirubarajan. Estimation with Applications to Tracking and Navigation [M]. John Wilely and Sons, Inc,2001.
[3]Y. Zhu. Multisensor decision and estimation fusion [M]. Kluwer academic publishers,2002.
[4]潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展[J].自动化学报,2003,29(4)：599-644.
[5]杨露菁,余华.多源信息融合理论与应用[M].北京：北京邮电大学出版社,2005.
[6]韩崇昭,朱洪艳,段战胜.多源信息融合[M].北京：清华大学出版社,2006.
[7]彭冬亮,文成林,薛安克.多传感器多源信息融合理论及应用[M].北京：科学出版社.2010.
[8]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.
[9]P. Kumar, A. Mittal. Addressing uncertainty in multi-modal fusion for improved object detection in dynamic environment [J].Information Fusion,2010,11(4):311-324.
[10]B. Khaleghi, A. Khamis, F. O.Karray, S. N.Razzavi. Multisensor data fusion:A review of the state-of-the art [J].Information Fusion,2013,14(1):28-44.
[11]张高煜.多传感器多源信息融合理论及应用[D].西安电子科技大学,2006.
[12]Y. Xia, M. Fu, G. Liu. Analyais and Synthesis of Netwoked Control Systems [M]. New York: Springer,2011.
[13]Y. Zhu, J. Zhou, X. Shen, E. Song et al. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods [M]. CRC Press,2012.
[14]潘泉,王增福,梁彦,杨峰,刘准钆.信息融合理论的基本方法与进展(Ⅱ)[J].控制理论与应用,2012,29(10)：1233-1244.
[15]B. Chen, Li Yu, W.A. Zhang, H. Song. Netorked Multi-Sensor Fusion Estimation with Delays, Packet Losses and Missing Measurements [A].12th International Conference on Control, Au-tomation, Robotics and Vision [C]. Guangzhou, China,2012:695-700.
[16]E.F. Nakamura. Information Fusion for Wireless Sensor Networks:Methods, Models, and Clas-sifications [J]. ACM Computing Surveys,2007,39(3):Article 9.
[17]张瑞华.基于能量效率的无线传感器网络关键技术研究[D].山东大学,2007.
[18]A. Oka, L. Lampe. Energy Efficient Distributed Filtering with Wireless Sensor Networks [J]. IEEE Transactions on Signal Processing,2008,56(5):2062-2075.
[19]J.P. Hespanha, P. Naghshtabrizi, Y. G. Xu. A survey of recent results in networked control systems [J]. Proceedings of The IEEE,2007,95(1):138-161.
[20]W.P.M. H.Heemels, A. R.Teel, N. Wouw, et al. Networked Control Systems with Communica-tion Constraints:Tradeoffs Between Transmission Intervals, Delays and Performance [J]. IEEE Transactions on Automatic Control,2010,55(8):1781-1796.
[21]张文安.网络化控制系统的时延与丢包问题研究[D].浙江工业大学,2010.
[22]W.S. Song, R.W. Brockett. System with finite communication bandwidth constraints-I:state es-timation problems [J]. IEEE Transactions on Automatic Control,1997,42(9):1294-1299.
[23]V. Malyavej and A. V. Savkin. The Problem of optimal robust Kalman state estimation via limited capacity digital communication channels [J]. Systems and Control Letters,2005,54(3):283-292.
[24]M. Sahebsara, T. Chen, S.L. Shah. Optimal H2 filtering with random sensor delay, multiple packet dropouts and uncertain observations [J]. International Journal of Control,2007,80:292-301.
[25]王宝凤.网络化控制系统中状态估计问题的研究[D].大连海事大学,2010.
[26]J. Ma, S. Sun. Optimal linear estimators for systems with random sensor delays, multiple packet dropouts and uncertain observations [J]. IEEE Transactions on Signal Processing,2011,59: 5181-5192.
[27]H. Zhang, G. Feng, C. Han. Linear estimation for random delay systems [J]. System Control Letters,2011,60(7):450-459.
[28]马静.网络化控制系统的建模与融合估计[D].黑龙江大学,2012.
[29]J.J. Xiao, A. Ribeiro, Z.Q. Luo, G.B. Giannakis. Distributed compression estimation using wire-less sensor networks [J]. IEEE Signal Processing Magazine,2006,7:27-41.
[30]A. Ribeiro, I.D. Schizas, S.I. Roumeliotis, G.B. Giannakis. Kalman filtering in wireless sensor networks [J]. IEEE Control System Magazine,2010,4:66-86.
[31]R. Singer, A.J. Kanyuck. Computer control of multiple site track correlation [J]. Automation, 1971,7:455-464.
[32]D. Willner, C.B. Chang, D.A. Castanon, et al. Kalman filter algorithm for a multisensor system [A]. Proceedings of IEEE Conferences on Decision and Control including the 15th Symposium on Adaptive Processes [C].1976,15:570-574.
[33]M.F. Hassan, G. Salut, M.G. Singh et al. A decentralized computational algorithm for the global Kalman filter [J]. IEEE Transactions on Automatic Control,1978,23(2):262-268.
[34]C.Y. Chong. Hierarchical estimation [A]. Procceedings of the 2nd MIT/ONR Workshop on C3 systems [C]. Monterey, California,1979.
[35]X.R Li, Y. Zhu, J. Wang, C.Z. Han. Optimal linear estimation fusion-part Ⅰ:unified fusion rules [J]. IEEE Transactions on Information Theory,2003,49:2192-2208.
[36]X.R. Li, J. Wang. Unified optimal linear estimation fusion-Part Ⅱ:Discussions and examples [A]. Procceedings of International Conference on Information Fusion [C]. USA,2000:MoC2.18-MoC2.25.
[37]X.R. Li, P. Zhang. Optimal linear estimation fusion-part Ⅲ:Cross-correlation of local estimation errors [A]. Procceedings of International Conference on Information Fusion [C]. USA,2001: WeB1.11-WeB1.18.
[38]X.R. Li, K.S. Zhang. Optimal linear estimation fusion-part Ⅳ:Optimality and efficiency of dis-tributed fusion [A]. Procceedings of International Conference on Information Fusion [C]. USA, 2001:WeB1.19-WeB 1.26.
[39]Z. Duan, X.R. Li. Lossless Linear transformation of sensor data for distributed estimation fusion [J]. IEEE Transactions on Singal Processing,2011,59:362-372.
[40]Y. Gao, W.J. Jia, X.J. Sun, Z.L. Deng. Self-Tuning multisensor weighted measurement fusion Kalman filter [J]. IEEE Transactions on Aerospace and Electronic Systems,2009,45(1):179-191.
[41]Y. Gao, C. Ran, Z.L. Deng. Weighted measurement fusion Kalman filer based on linear unbiased minimum variance criterion [A]. Joint 48th IEEE Conference on Decesion and Control and 28th Chinese Control Conference [C]. Shanghai, China,2009:7593-7598.
[42]冉陈键.最优和自校正多传感器加权观测融合卡尔曼滤波器[D].黑龙江大学,2011.
[43]郝钢,叶秀芬,陈亭.加权观测融合非线性无迹卡尔曼滤波算法[J].控制理论与应用,2011,28(6)：753-758.
[44]Q. Gan, C.J. Harris. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion [J]. IEEE Transactions on Aerospace and Electronic Systems,2001,37: 273-280.
[45]邓自立.两种最优观测融合方法的功能等价性[J].控制理论与应用,2006,23(2)：319-323.
[46]H.M. Chen, T. Kirubarjan, Y. Bar-Shalom. Track-to-track fusion versus centralized estimation: Theory and application [J]. IEEE Transactions on Aerospace and Electronic Systems,2003, 39(2):386-411.
[47]Y. Bar-shalom, L. Capmo. The effect of the common process noise on the two-sensor fused track covariance [J]. IEEE Transactions on Aerospace and Electronic Systems,1986,22(6):803-805.
[48]Y. Bar-shalom. On the track-to-track correlation problem [J]. IEEE Transactions on Automatic Control,1981,26:571-572.
[49]G.J. Bierman, M.R. Belzer. A decentralized square root information filter/smoother [A]. Pro-ceedings of 24th IEEE Conference on Decision and Control [C]. Ft. Lauderdale, Fl, USA,1985: 1902-1905.
[50]S. Roy, R.H. Hashem, A.J. Laub. Square root filter for parallel Kalman filtering using reduced-order local filters [J]. IEEE Transactions on Aerospace and Electronic Systems,1991,27(2): 276-288.
[51]D.A. Castanon, D. Teneketzis. Distributed estimaiton algorithm for nonlinear system [J]. IEEE Transactions on Automatic Control,1985,30(5):418-425.
[52]N.A. Carlson. Federated square root filter for decentralized parallel processes[J]. IEEE Transac-tions on Aerospace and Electronic Systems,1990,26:517-525.
[53]K.H. Kim. Development of track to track fusion algorithm [A]. American Control Conference [C]. Maryland, America,1994:1037-1041.
[54]S.L. Sun, Z.L. Deng. Multi-sensor optimal information fusion Kalman filter [J]. Automatica, 2004,40:1017-1023.
[55]Z.L. Deng, y. Gao, L. Mao, et al. New approach to information fusion steady-state Kalman filter-ing [J]. Automatica,2005,41(10):1695-1707.
[56]S.L. Sun. Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother [J]. Automatica,2005,41(12):2153-2159.
[57]石莹.广义系统的状态估计[D].哈尔滨工业大学,2007.
[58]L. Chen, P.O. Arambel, R.K. Mehra. Estimation under unknown correlation:covariance intersec-tion revisited [J]. IEEE Transactions on Automatic Control,2002,47:1879-1882.
[59]Z.L. Deng, P. Zhang, W.J. Qi, et al. Sequential covariance intersection fusion Kalman filter [J]. Information Sciences,2012,189:293-309.
[60]金学波.多传感器状态融合估计理论与应用研究[D].浙江大学,2003.
[61]李庆华.H∞多传感器状态融合估计理论与应用研究[D].山东大学,2009.
[62]冯肖亮,文成林,刘伟峰等.基于多传感器的序贯式融合有限域H∞滤波方法[J].自动化学报,2013,39(9)：1523-1532.
[63]V. Shnayder, M. Hempstead, B. Chen, et al. Simulating the power consumption of large-scale sensor network applications [A]. SenSys [C].2004:188-200.
[64]J. A.Gubner. Distributed estimaiton and quantization [J]. IEEE Transactions on Information The-ory,1993,39(4):1456-1459.
[65]Z.Q. Luo. Universal decentralized estimation in a bandwidth constrained sensor networks [J]. IEEE Transactions on Information Theory,2005,51(6):2210-2219.
[66]A.Riberio, G. B.Giannakis. Bandwidth-constrained distributed estimation for wireless sensor networks-Part Ⅰ:Gaussian case [J]. IEEE Transactions on Signal Processing,2006,54:1131-1143.
[67]A. Riberio, G. B.Giannakis, S. I.Roumeliotis. SOI-KF:Distributed Kalman filtering with low-cost communication using the sign of innovations [J]. IEEE Transactions on Signal Processing,2006, 54:4782-4795.
[68]J. Fang, H. Li. Distributed adaptive quantization for wireless sensor networks:From delat modu-lation to maximum likelihood [J]. IEEE Transactions on Signal Processing,2008,56(10):5246-5247.
[69]S.L. Sun, J.Y. Lin, L.H. Xie, W.D. Xiao. Quantized Kalman filtering [A]. The 22nd IEEE Inter-national Symposium on Intelligent Control [C]. Singapore,2007:7-12.
[70]C.L. Wen, Q.B Ge, X.F. Tang. Kalman filtering in a bandwidth constrained sensor network [J]. Chinese Journal of Electronics,2009,18(4):713-718.
[71]X. Shen, Y. Zhu, Z. You. An efficient sensor quantization algorithm for decentralized estimation fusion [J]. Automatica,2011,47:1053-1059.
[72]陈军勇,邬依林,祁恬.无线传感器网络分布式量化卡尔曼滤波[J].控制理论与应用,2011,28(12)：1729-1739.
[73]J.J. Xiao, S. Cui, Z.Q. Luo, A. J.Goldsmith. Power scheduling of universal decentralized estima-tion in sensor networks [J]. IEEE Transactions on Signal Processing,2006,54(2):413-422.
[74]J. Li, G. AlRegib. Distributed estimation in energy-constrained wireless sensor networks [J]. IEEE Transactions on Signal Processing,2009,57(10):3746-3758.
[75]J. Fang, H. Li. Hyperplane-based vector quantization for distributed estimation in wireless sensor networks [J]. IEEE Transactions on Information Theory,2009,55(12):5682-5699.
[76]Z.Q. Luo, J. N.Tsitiklis. Data fusion with minimal communication [J]. IEEE Transactions on Information Theory,1994,40(5):1551-1563.
[77]Z.Q. Luo, J. N.Tsitiklis. Data fusion with minimal communication [J]. IEEE Transactions on Singal Processing,2005,53(5):1631-1639.
[78]宋恩彬.信息融合与处理中几个问题的进展[D].四川大学,2007.
[79]I. D.Schizas, G. B.Giannakis, Z.Q. Luo. Distributed estimation using reduced-dimensionality sen-sor observations [J]. IEEE Transactions on Singal Processing,2007,55(8):4284-4299.
[80]A. S.Behbahani, A. M.Eltawil, H. Jafarkhani. Linear decentralized estimation of correlated data for power-constrained wireless sensor networks [J]. IEEE Transactions on Singal Processing, 2012,60(11):6003-6016.
[81]J.J. Xiao, S. Cui, Z.Q. Luo, A. J.Goldsmith. Linear coherent decentralized estimation [J]. IEEE Transactions on Singal Processing,2008,56(2):757-770.
[82]J. Fang, H. Li. Joint dimension assignment and compression for distributed multisensor estimation [J]. IEEE Singal Processing Letters,2008,15:174-177.
[83]H. Zhu, I. D.Schizas, G. B.Giannakis. Power-efficient dimensionality reduction for distributed channel-aware Kalman tracking using WSNs [J]. IEEE Transactions on Singal Processing,2009, 57(8):3193-3207.
[84]H. Chen, X.R. Li. On track fusion with communicaiton constraints [A]. Proceedings of the 10th International Conference on Information Fusion [C].2007:9-12.
[85]W. Koch. On optimal distributed Kalman filtering and retrodiction at arbitary communication rates for maneuvering targets [A]. Proceedings of IEEE International Conferences on Multisensor Fusion and Integration for Intelligent Systems [C]. Seoul, Korea,2008:457-462.
[86]R. S.Blum. Ordering for estimation and optimization in energy efficient sensor networks[J]. IEEE Transactions on Singal Processing,2011,59(6):2847-2856.
[87]G. Battistelli, A. Benavoli, L. Chisci. State estimation with remote sensors and intermittent trans-missions [J]. Systems and Control Letters,2011,61:155-164.
[88]W.A. Zhang, Li Yu, X. Qiu, B. Chen. Energy-efficient fusion estimation for wireless sensor net-works with packet losses [A]. Proceedings of the 30th Chinese Control Conference [C]. Yantai, China,2011:6408-6412.
[89]W.A. Zhang, G. Feng, L. Yu. Multi-rate distributed fusion estimation for sensor networks with packet losses [J]. Automatica,2012,48:2016-2028.
[90]A. Chiuso, L. Schenato. Information fusion strategies and performance bounds in packet-drop networks [J]. Automatica,2011,47:1304-1316.
[91]Y. Liang, T. Chen, Q. Pan. Multi-rate stochastic H∞ filteing for networked multi-sensor fusion [J]. Automatica,2010,46:437-444.
[92]J. Ma, S. Sun, Information fusion estimators for systems with multiple sensors of different packet dropout rates [J]. Information Fusion,2011,12:213-222.
[93]R.K. Varshney, P.K. Varshney. Recursive estimation with uncertain observations in multisensor environment [J]. IEE Proceedings,1986,133:527-533.
[94]F. O.Hounkpevi, E. E.Yaz. Robust minimum variance linear state estimators for multiple sensors with different failure rates [J]. Automatica,2007,43:1274-1280.
[95]N. Lv, S.L. Sun. Scalar-weighted fusion estimators for systems with multiple sensors and multiple delayed measurements [A]. Proceedings of the 48th IEEE Conference on Decision and Control [C]. Shanghai, China,2009:7599-7602.
[96]孙书利,吕楠.带有色观测噪声多传感器多重时滞系统分布式融合滤波器[J].自动化学报,2009,35(1)：46-53.
[97]J. Sun, C. Zhang, J. Gu. Decentralized optimal fusion filtering for multi-sensor multi-delay sin-gular systems [J]. Circuits Systems and Signal Processing,2012,31:163-176.
[98]X.J. Sun, Z.L. Deng. Information fusion wiener filter for the multisensor multichannel ARMA signals with time-delayed measurements [J]. IET Signal Processing,2009,3:403-415.
[99]Y. Xia, J. Shang, J. Chen, G.P. Liu. Networked data fusion with packet losses and variable delays [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics,2009,39(5): 1107-1120.
[100]I. Penarrocha, R. Sanchis, P. Albertos. Estimation in multisensor networked systems with scarce measurements and time-varying delays [J]. Systems Control Letters,2012,61:555-562.
[101]H. Zhang, Y. Shi, A.S. Mehr. Robust weighted H∞ filtering for networked systems with intermit-tent measurements of multiple sensors [J]. International Jouranl of Adaptive Control and Signal Processing,2011,25:313-330.
[102]M. Liu, M. Qiu, S. Zhang, Z. Lin. Robust H∞ fusion filtering for discrete-time nonlinear delayed systems with missing measurement [A]. Proceedings of the 2010 American Control Conference [C].2010:6703-6808.
[103]M. Liu, D. Qi, S. Zhang, et al. Optimal H∞ fusion filters for a class of discrete-time intelligent systems with time delays and missing measurement [J]. Neurcomputing,2011,74:3741-3752.
[104]R. Rahman, M. Alanyali, V. Saligrama. Distributed tracking in multihop sensor networks with communication delays [J]. IEEE Transactions on Singal Processing,2007,55(9):4656-4668.
[105]L. Schenato. Optimal sensor fusion for distributed sensors subject to random delay and packet loss [A]. Proceedings of the 46th IEEE Conference on Decision and Control [C].2007:1547-1552.
[106]S.L. Sun, W. Xiao. Distributed weighted fusion estimation with random delays and packet drop-ping [J]. Circuits Systems and Signal Processing,2007,26(4):591-605.
[107]J. Ma, S. Sun. Centralized fusion estimators for multisensor systems with random sensor delays, multiple packet dropouts and uncertain observations [J]. IEEE Sensors Journal,2013,13(4): 1228-1235.
[108]R.A. Horn, C.R. Johnson. Topics in matrix analysis [M]. New York, Cambrideg University Press, 1991.
[109]F. Hu, Y. Wang, H. Wu. Mobile telemedicine sensor networks with low-energy data query and network lifetime considerations [J]. IEEE Transactions on Mobile Computing,2006,5(4):404-417.
[110]C.K. Chui, G. Chen. Kalman filtering with real-time applications [M]. Springer,1999.
[111]X.R. Li, V.P. Jilkov. Survey of maneuvering target tracking, part I:Dynamic models [J]. IEEE Transactions on Aerospace and Electronic Systems,2003,39:1333-1364.
[112]俞立.鲁棒控制-线性矩阵不等式处理方法[M].北京：清华大学出版社,2002.
[113]M. Fu, L. Xie. The sector bound appraoch to quantized feedback control [J]. IEEE Transactions on Automatic Control,2005,50:1698-1711.
[114]C.E. de Souza, L. Xie. On the discrete-time bounded real lemma with application in the charac-terization of static feedback H∞ controllers [J]. Systems Control Letters,1992,18:61-71.
[115]孙利民,李建中,陈渝,等.无线传感器网络[M].北京：清华大学出版社,2005.
[116]A.H. Jazwioski. Stochastic Processes and Filtering Theory [M]. New York:Academic,1970.
[117]S.D. Wang, T.S. Kuo, C.F. Hsu. Trace bounds on the solution of the algebraic matrix riccati and lyapunov equation [J]. IEEE Transactions on Automatic Control,1986,31:654-656.
[118]R. M.Palhares, P. L.D.Peres. Optimal filtering schemes for linear discrete-time systems:a linear matrix inequality approach [J]. International Jouranl of Systems Science,1998,29(6):587-593.
[119]R. M.Palhares, P. L.D.Peres. LMI approach to the mixed H2/H∞ filtering design for discrete-time uncertain systems [J]. IEEE Transactions on Aerospace and Electronic Systems,2001,37(1): 292-296.
[120]H. Gao,J. Lam, L. Xie, C.Wang. New approach to mixed H2/H∞ filtering for polytopic discrete-time systems [J]. IEEE Transactions on Signal Processing,2005,53(8):3183-3192.
[121]M. Sznaier. New exact solution to general SISO mixed H2/H∞ problems via convex optimization [J]. IEEE Transactions on Automatic Control,1994,39(12):2511-2517.
[122]R. W.Eustace, B. A.Woodyatt, G. L.Merrington, A. Runacres. Fault signatures obtained from fault implant tests on an F404 Engine [J]. Transactions of the ASME,1994,116:176-183.
[123]W.W. Che, G.H. Yang. Quantised H∞ filter design for discrete-time systems [J]. International Journal of Control,2009,82(2):195-206.
[124]J. Hu, Z. Wang, H. Gao, L. K.Stergioulas. Robust sliding mode control for discrete stochastic systems with mixed time delays, randomly occurring uncertainties, and randomly occurring non-linearties [J]. IEEE Transactions on Industrial Electronics,2012,59(7):3008-3015.
[125]J. Nilson. Real-time control systems with delays [D]. Lund Institute of Technology,1998.
[126]H. Gao, X. Meng, T. Chen. Stabilization of networked control systems with a new delay charac-terization [J]. IEEE Transactions on Automatic Control,2008,53:2142-2148.
[127]H. Dong, Z. Wang, H. Gao. Robust H∞ filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts [J]. IEEE Transactions on Signal Processing,2010,58:1957-1966.
[128]R. Piziak, P.L. Odell. Full rank factorization of matrices [J]. Mathematics Magazine,1999,72: 193-201.
[129]F. Wang, V. Balakrishnan. Robust Kalman filters for linear time-varying systems with stochastic parameter uncertainties[J]. IEEE Transactions on Signal Processing,2002,50:803-813.
[130]T. Kailath, A.H. Syayed, B. Hassibi. Linear Estimation [M]. Prentice Hall,2000.