最优加权观测融合状态估值器及其应用
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摘要
近年来,随着计算机技术、通讯技术的发展,多传感器信息融合技术得到了迅速发展,并成为当前信息处理领域一个十分活跃的研究热点。多传感器信息融合滤波的目的是:基于由每个传感器得到的关于系统状态或信号的局部观测或局部估计信息,在某种最优融合准则下得到系统状态或信号的融合估计,其精度高于每个局部估计精度。
本文应用加权最小二乘(WLS)法,基于Riccati方程,分别对带相同或不同观测阵以及相关观测噪声的多传感器线性离散随机系统,提出两种加权观测融合Kalman滤波算法;对带相同观测阵、相关观测噪声以及相关的输入和观测噪声的多传感器线性离散随机系统,提出一种加权观测融合Kalman滤波算法;并基于信息滤波器证明了上述加权观测融合Kalman滤波算法同集中式观测融合Kalman滤波算法相比是完全功能等价的,因而具有全局最优性。还提出了相应的加权观测融合稳态Kalman滤波算法和多传感器加权观测融合状态分量解耦Wiener估值算法,它们具有渐近全局最优性。给出了它们在ARMA信号观测融合Wiener滤波中的应用。在跟踪系统中大量的仿真例子说明了它们的有效性。
With the development of computer and communication technology, multisensor information fusion technology develops rapidly and becomes an active research focus in current information processing domain during the past late years. The objective of multisensor information fusion filtering are to find the fusion estimators for the state or signal under some optimal fusion rules, the accuracy of which is higher than that of each local estimator, based on the local measurements or the local estimates for the state or signal of each sensor.
For the multisensor linear discrete time-invariant stochastic control systems with same and different measurement matrices and with correlated measurement noises, using the weighted least squares (WLS) method, based on Riccati equation, two weighted measurement fusion Kalman filtering algorithms are presented respectively in this paper. For the multisensor linear discrete time-invariant stochastic systems with same measurement matrices and with correlated measurement noises and correlated input and measurement noises, a weighted measurement fusion Kalman filtering algorithms is also presented. Based on the Klaman filter in the information filter form, it is proved that they are completely functionally equivalent to the centralized measurement fusion Kalman filtering algorithm, so that they have global optimality. The corresponding weighted measurement fusion steady-state Kalman filtering algorithms and the weighted measurement fusion state component decoupled Wiener estimate algorithm are also presented, which have the asymptotical global optimality, and their applications in ARMA signal measurement fusion Wiener filtering are given. The simulation examples for the tracking system show their effectiveness.
本文应用加权最小二乘(WLS)法,基于Riccati方程,分别对带相同或不同观测阵以及相关观测噪声的多传感器线性离散随机系统,提出两种加权观测融合Kalman滤波算法;对带相同观测阵、相关观测噪声以及相关的输入和观测噪声的多传感器线性离散随机系统,提出一种加权观测融合Kalman滤波算法;并基于信息滤波器证明了上述加权观测融合Kalman滤波算法同集中式观测融合Kalman滤波算法相比是完全功能等价的,因而具有全局最优性。还提出了相应的加权观测融合稳态Kalman滤波算法和多传感器加权观测融合状态分量解耦Wiener估值算法,它们具有渐近全局最优性。给出了它们在ARMA信号观测融合Wiener滤波中的应用。在跟踪系统中大量的仿真例子说明了它们的有效性。
With the development of computer and communication technology, multisensor information fusion technology develops rapidly and becomes an active research focus in current information processing domain during the past late years. The objective of multisensor information fusion filtering are to find the fusion estimators for the state or signal under some optimal fusion rules, the accuracy of which is higher than that of each local estimator, based on the local measurements or the local estimates for the state or signal of each sensor.
For the multisensor linear discrete time-invariant stochastic control systems with same and different measurement matrices and with correlated measurement noises, using the weighted least squares (WLS) method, based on Riccati equation, two weighted measurement fusion Kalman filtering algorithms are presented respectively in this paper. For the multisensor linear discrete time-invariant stochastic systems with same measurement matrices and with correlated measurement noises and correlated input and measurement noises, a weighted measurement fusion Kalman filtering algorithms is also presented. Based on the Klaman filter in the information filter form, it is proved that they are completely functionally equivalent to the centralized measurement fusion Kalman filtering algorithm, so that they have global optimality. The corresponding weighted measurement fusion steady-state Kalman filtering algorithms and the weighted measurement fusion state component decoupled Wiener estimate algorithm are also presented, which have the asymptotical global optimality, and their applications in ARMA signal measurement fusion Wiener filtering are given. The simulation examples for the tracking system show their effectiveness.
引文
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[3]潘泉,于昕,程咏梅,张洪才.信息融合理论的基本方法与进展.自动化学报, 2003,29(4):599-616
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[7] Kam M. Rorres C , Chang W, Zhu X. Performance and geometric interpretation for decision fusion with memory. I EEE Transactions on Ssystems , Man , and Cybernetic 2 Part A : Systems and Humans , 1999 , 29 (1) : 52-62
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[15] Aziz A M , Tummala M , Cristi R. Fuzzy logic data correlation approach in multisensor2multitarget tracking systems. Signal Processi ng , 1999 ,76 (2) : 195-209
[16] Fayman J A , Pirjanian P , Christensen H , Rivlin E. Exploiting process integration and composition in the context of active vision. I EEE Transactions on Systems , Man , and Cybernetic Part C : A pplications and Reviews , 1999 ,29 (1) :73-86
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[19]崔平远,黄晓瑞.基于联合卡尔曼滤波的多传感器信息融合算法及其应用[J].电机与控制学报,2001, 5(3): 72-74
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[21]石松庆.信息融合与战术光电子系统[J].激光与红外,1997, 27(4): 195-200
[22]周丽莉,胡昌振.多传感器信息融合技术最新发展及其研究展望[J].现代引信,1997, (2):42-46
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[27]吕孝乐.数据融合技术研究及其在船舶组合导航中的应用[D].哈尔滨:哈尔滨工程大学,2002.
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[30]权太范.信息融合神经网络一模糊推理理论与应用[M].北京:国防工业出版社,2002.
[31]李玉榕.信息融合与智能处理的研究[D].杭州:浙江大学,2001.
[32]郭惠勇.多传感器信息融合技术的研究与进展[J].中国科学基金,2005,1:17-21.
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