多传感器标量加权最优信息融合稳态Ka lman 滤波器

提出一种新的标量加权多传感器线性最小方差意义下的最优信息融合准则．该准则考虑了局部估计误差之间的相关性，只需计算加权标量系数，避免了加权矩阵的计算，明显减小了计算量，便于实时应用．运用稳态Kalman滤波理论，基于该融合准则，给出了多传感器最优信息融合稳态Kalman滤波器．在所有局部滤波器达到稳态时，只需一次融合便可获得信息融合稳态滤波器，算法简单．仿真例子验证了其有效性．     

Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor

A unified multi-sensor optimal information fusion criterion weighted by scalars is presented in the linear minimum variance sense. The criterion considers the correlation among local estimation errors, only requires the computation of scalar weights, and avoids the computation of matrix weights so that the computational burden can obviously be reduced. Based on this fusion criterion and Kalman predictor, an optimal information fusion filter for the input white noise, which can be applied to seismic data processing in oil exploration, is given for discrete time-varying linear stochastic control systems measured by multiple sensors with correlated noises. It has a two-layer fusion structure. The first fusion layer has a netted parallel structure to determine the first-step prediction error cross-covariance for the state and the filtering error cross-covariance for the input white noise between any two sensors at each time step. The second fusion layer is the fusion center to determine the optimal scalar weights and obtain the optimal fusion filter for the input white noise. Two simulation examples for Bernoulli-Gaussian white noise filter show the effectiveness.     

Optimal multi-sensor Kalman smoothing fusion for discrete multichannel ARMA signals

Based on the multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, using white noise estimators, an optimal fusion distributed Kalman smoother is given for discrete multi-channel ARMA （autoregressive moving average） signals. The smoothing error cross-covanance matrices between any two sensors are given for measurement noises. Furthermore, the fusion smoother gives higher precision than any local smoother does.     

Multi-sensor optimal information fusion Kalman filter

This paper presents a new multi-sensor optimal information fusion criterion weighted by matrices in the linear minimum variance sense, it is equivalent to the maximum likelihood fusion criterion under the assumption of normal distribution. Based on this optimal fusion criterion, a general multi-sensor optimal information fusion decentralized Kalman filter with a two-layer fusion structure is given for discrete time linear stochastic control systems with multiple sensors and correlated noises. The first fusion layer has a netted parallel structure to determine the cross covariance between every pair of faultless sensors at each time step. The second fusion layer is the fusion center that determines the optimal fusion matrix weights and obtains the optimal fusion filter. Comparing it with the centralized filter, the result shows that the computational burden is reduced, and the precision of the fusion filter is lower than that of the centralized filter when all sensors are faultless, but the fusion filter has fault tolerance and robustness properties when some sensors are faulty. Further, the precision of the fusion filter is higher than that of each local filter. Applying it to a radar tracking system with three sensors demonstrates its effectiveness.     

多模型多传感器信息融合Kalman平滑器

基于标量加权的线性最小方差最优信息融合算法,对多模型多传感器离散线性随机系统,给出了一种分布式标量加权信息融合固定滞后Kalman平滑器.它只需计算加权标量系数,可减小在融合中心的计算负担.当各子系统存在稳态滤波时,又给出了标量加权信息融合稳态平滑器,它计算量小,便于实时应用.并给出了两个子系统之间的平滑误差互协方差阵的计算公式.仿真例子验证了其有效性.     

Optimal Fusion Reduced-Order Kalman Filters Weighted by Scalars for Stochastic Singular Systems

Based on the optimal fusion algorithm weighted by scalars in the linear minimum variance sense, a distributed optimal fusion reduced-order Kalman filter with scalar weights is presented for discrete-time stochastic singular systems with multiple sensors and correlated noises. It has higher accuracy than any local filter does. Compared with the distributed fusion filter weighted by matrices, it has lower accuracy but has reduced computational burden. Computation formula of cross-covariance matrix of the filtering errors between any two sensors is given. An example with three sensors shows the effectiveness.     

带有色观测噪声系统多传感器标量加权最优信息融合稳态Kalman滤波器

基于标量加权多传感器线性最小方差最优信息融合准则,对被多传感器观测的带有色观测噪声的离散线性随机控制系统,提出了一种具有两层融合结构的标量加权信息融合稳态Kalman滤波器,它等价于相应的带相关噪声系统的最优信息融合稳态Kalman预报器.最优信息融合稳态预报器可在所有局部预报器达到稳态时,通过一次融合获得,且任两个子系统之间的稳态预报误差互协方差阵可通过任选初值迭代求得,并证明了它的收敛性.通过将它应用到带三个传感器的雷达跟踪系统验证了其有效性.     

Robust weighted fusion Kalman predictors with uncertain noise variances

In this paper, the problem of designing weighted fusion robust time-varying Kalman predictors is considered for multisensor time-varying systems with uncertainties of noise variances. Using the minimax robust estimation principle and the unbiased linear minimum variance (ULMV) rule, based on the worst-case conservative system with the conservative upper bounds of noise variances, the local and five weighted fused robust time-varying Kalman predictors are designed, which include a robust weighted measurement fuser, three robust weighted state fusers, and a robust covariance intersection (CI) fuser. Their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties of noise variances. Their robustness is proved based on the proposed Lyapunov equation approach. The concept of the robust accuracy is presented, and the robust accuracy relations are proved. The corresponding steady-state robust local and fused Kalman predictors are also presented, and the convergence in a realization between the time-varying and steady-state robust Kalman predictors is proved by the dynamic error system analysis (DESA) method and the dynamic variance error system analysis (DVESA) method. Simulation results show the effectiveness and correctness of the proposed results.     

Self-tuning weighted measurement fusion Kalman filter and its convergence

For multisensor systems, when the model parameters and the noise variances are unknown, the consistent fused estimators of the model parameters and noise variances are obtained, based on the system identification algorithm, correlation method and least squares fusion criterion. Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter, a self-tuning weighted measurement fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, the convergence of the self-tuning weighted measurement fusion Kalman filter is proved, i.e., the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization. Therefore, the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality. One simulation example for a 4-sensor target tracking system verifies its effectiveness.     

多传感器信息融合Wiener滤波器和平滑器

应用现代时间序列分析方法,基于ARMA新息模型和增广状态空间模型,应用标量加权最优融合准则,对于带白色和有色观测噪声的ARMA信号,提出了多传感器分布式最优信息融合Wiener滤波器和平滑器,其中给出了计算局部平滑误差方差和互协方差的计算公式,它们可被用于计算最优加权系数。同单传感器情形相比,可提高平滑器的精度。一个三传感器目标跟踪系统的仿真例子说明其有效性。     

标量及对角阵加权的序贯估计融合算法研究

针对多传感器分布式估计融合系统,在最小化估计误差的协方差矩阵迹的准则下,采用标量加权及对角阵加权融合方法,提出了估计误差相关条件下的序贯处理式最优估计融合Kalman滤波器。该融合滤波器以两传感器估计融合算法为基础,对传感器采集信息依次进行融合计算,得到多传感器融合结果。比较两种算法与局部滤波器的估计精度,并进行了仿真。仿真结果表明了基于加权估计融合的序贯处理算法的可行性和有效性。     

按对角阵加权信息融合Kalman滤波器

为了克服按矩阵加权信息融合非稳态Kalman滤波器的在线计算负担大的缺点,和按标量加权融合Kalman滤波器精度较低的缺点,应用现代时间序列分析方法,提出了按对角阵加权的线性最小方差多传感器信息融合稳态Kalman滤波器.它等价于状态分量按标量加权信息融合Kalman滤波器,实现了解耦信息融合Kalman滤波器.它的精度和计算负担介于按矩阵和按标量加权融合器两者之间,且便于实时应用.为了计算最优加权,提出了计算稳态滤波误差方差阵和协方差阵的Lyapunov方程.一个三传感器的雷达跟踪系统的仿真例子说明了其有效性.     

非线性离散系统的信息融合最优预见控制

将信息融合思想引入非线性离散系统的预见控制,提出了信息融合最优预见控制,将对控制系统的所有性能要求和系统动力学转化为可融合信息;然后从信息融合估计的角度,使原问题转化为求控制量的"融合估计"问题.推导了基于卡尔曼滤波器的最优预见控制算法,讨论了预见步数的选取问题,并通过对机械手的转移控制仿真研究说明了信息融合最优预见控制算法的有效性.     

Distributed fusion Kalman filtering under binary sensors

Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.     

一种面向多无人机协同感知的分布式融合估计方法

针对基于扩展卡尔曼滤波的融合估计算法存在线性化误差,且分布式融合计算复杂等问题,基于无色变换、交互多模型和信息滤波,采用递阶分布式融合估计结构,提出一种分布式无色信息滤波算法.该方法中的无色变换能够保证更高的估计精度,交互多模型使其具有更好的鲁棒性,信息形式的卡尔曼滤波使融合估计计算变得简单.仿真结果表明,该算法能够提高多无人机融合估计性能.     

快速对角阵权系数协方差交叉融合容积卡尔曼滤波器

针对互协方差信息未知的多传感器系统,本文提出了一种快速对角阵权系数协方差交叉融合算法(FDCI).本文首先提出了一种对角阵权系数协方差交叉融合(DCI)方案,并证明了所提出DCI算法在融合估计精度上高于经典批处理CI融合(BCI)算法.在此基础之上,针对非线性等复杂的互协方差未知的多传感器系统,提出FDCI算法,并证明了所提出FDCI算法的无偏性及鲁棒精度. FDCI融合算法虽然在融合估计精度上低于DCI,但FDCI无需进行多权系数的非线性代价函数的优化问题,进而大大降低了计算负担,提高了系统的实时性.最后,结合容积卡尔曼滤波算法(CKF)提出了快速对角阵权系数协方差交叉融合容积卡尔曼滤波算法.仿真实例验证了所提出算法的正确性和有效性.     

基于Riccati方程的自校正解耦融合Kalman滤波器

对于带未知噪声方差的多传感器系统,用相关方法给出了噪声方差的在线估值器,进而基于Riccati方程和按分量标量加权最优融合规则,提出了自校正分量解耦信息融合Kalman滤波器.用动态误差系统分析方法证明了自校正融合Kalman滤波器按实现收敛于最优融合Kalman滤波器,因而具有渐近最优性.一个3传感器跟踪系统的仿真例子说明了其有效性.     

Distributed fusion cubature Kalman filters for nonlinear systems

This paper is concerned with the distributed fusion estimation problem for multisensor nonlinear systems. Based on the Kalman filtering framework and the spherical cubature rule, a general method for calculating the cross‐covariance matrices between any two local estimators is presented for multisensor nonlinear systems. In the linear unbiased minimum variance sense, based on the cross‐covariance matrices, a distributed fusion cubature Kalman filter weighted by matrices (MW‐CKF) is presented. The proposed MW‐CKF has better accuracy and robustness. An example verifies the effectiveness of the proposed algorithms.     

多传感器时滞系统信息融合最优Kalman滤波器

基于线性最小方差最优加权融合估计算法,对多传感器的离散线性状态时滞随机系统,给出了一种非增广分布式加权融合最优Kalman滤波器.推导了状态时滞系统任两个传感器子系统之间的滤波误差互协方差阵的计算公式.它与状态增广加权融合滤波器具有相同的精度.与每个传感器的局部滤波器相比,分布式融合滤波器具有更高的精度.与状态和观测增广最优滤波器相比,具有较小的精度.但避免了增广所带来的高维计算和大的空间存储,可减小计算负担.仿真例子验证了其有效性.