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.     

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.     

基于强跟踪滤波器的多传感器信息融合应用研究

在对经典Kalman滤波器和强跟踪Kalman滤波器分析的基础上,给出了改进的强跟踪Kalman滤波器方法,并进一步给出了改进的强跟踪Kalman滤波器分布式信息融合方法。该方法底层采用改进的强跟踪器滤波,上层采用估计误差方差最小方法进行分布式信息融合,信息融合结果精度高,同时对突变信号有很强的实时跟踪能力。仿真结果表明该方法的有效性和可靠性。     

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.     

协方差交叉融合鲁棒Kalman滤波器

对于带未知互协方差的两传感器系统,提出一种协方差交叉（CI）融合鲁棒稳态Kalman滤波器,它关于未知互协方差具有鲁棒性.严格证明了该滤波器的实际精度高于每个局部滤波器的精度,但低于带已知互协方差的最优融合Kalman滤波器的精度.基于协方差椭圆给出了精度关系的几何解释.进一步将上述结果推广到一般多传感器情形.一个跟踪系统的Monte-Carlo仿真例子表明,其实际精度接近于带已知互协方差的最优融合器的精度.     

快速信息融合Kalman滤波器

应用现代时间序列分析方法,在标量加权线性最小方差融合准则下,提出一种多传感器快速信息融合稳态Kalman滤波器.基于ARMA新息模型计算稳态Kalman滤波器增益,提出了计算传感器之间的滤波误差方差阵和协方差阵的Lyapunov方程,它可用迭代法求解,并证明了迭代解的指数收敛性.与基于Riccati方程按矩阵加权的信息融合Kalman滤波器相比,可明显减小计算负担,便于实时应用,可用于设计含未知噪声统计系统的信息融合自校正Kalman滤波器.最后以目标跟踪系统的一个仿真例子说明了其有效性.     

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.     

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。     

广义离散系统多传感器信息融合Kalman滤波器

考虑了广义离散随机线性系统的多传感器信息融合状态估计问题．在广义系统无脉冲的假设条件下。通过等价变换将其转化为正常系统．应用经典Kalman滤波方法,在线性最小方差信息融合准则下,提出了按矩阵加权的广义系统多传感器信息融合稳态Kalman状态滤波器．仿真结果说明了算法的有效性。     

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.     

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.     

Distributed optimal component fusion weighted by scalars for fixed-lag Kalman smoother

Based on the optimal fusion criterion in the linear minimum variance sense, a distributed optimal fusion fixed-lag Kalman smoother with a three-layer fusion structure is given for the discrete time-varying linear stochastic control systems with multiple sensors and correlated noises. Its components are estimated by scalar weighting fusion, respectively. It only requires in parallel a series of computations of the weighted scalars, and avoids the computations of the weighted matrices, so that the computational burden can obviously be reduced. Further, the steady-state fusion smoother is also given for the discrete time-invariant linear stochastic control systems. The scalar weights can be obtained by fusing once after all local estimations reach steady state. It can reduce the online computational burden. Also, the computation formulas of smoothing error cross-covariance matrices are given. Two simulation examples show the performance.     

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.     

多传感器集中式增量卡尔曼滤波融合算法

在单个传感器的状态估计系统中,标准的增量卡尔曼滤波方法可以有效消除量测系统误差。对于多传感器情况,标准算法失效。针对该问题,提出了多传感器集中式增量卡尔曼滤波融合算法,即:增量卡尔曼滤波的扩维融合算法和增量卡尔曼滤波的序贯融合算法。在标准增量卡尔曼滤波算法的基础上,结合扩维融合和序贯融合的思想来实现多传感器数据的融合。实验结果表明,当存在量测系统误差时,提出的集中式融合算法与传统的集中式融合算法相比,提高了滤波精度,并且能够成功地消除量测系统误差。