多传感器异步采样系统的顺序融合

针对现有基于伪量测的异步融合算法存在实时性差、融合时刻计算负荷大以及人为引入噪声相关等问 题,提出了一种新的基于采样点顺序离散化思想的顺序式异步融合算法．该算法首先将各个传感器的测量值在融合 中心的坐标系中和时钟下进行映射统一;然后,选取融合周期内各采样时刻对连续状态系统进行顺序离散化,从而 获得本周期内各采样点间的状态方程和相应的测量方程．最终,使用线性最小均方误差意义下最优的线性卡尔曼滤 波器实现本周期内异步采样量测的顺序滤波融合．仿真分析表明,该算法和基于伪量测的异步融合算法相比具有较 少的计算量、较好的实时性和较高的估计融合精度．     

带时间相关乘性噪声多传感器系统的分布式融合估计

研究带时间相关乘性噪声多传感器系统的分布式融合估计问题,其中时间相关的乘性噪声满足一阶高斯-马尔科夫过程.通过引入虚拟状态和虚拟过程噪声,构建了虚拟状态的递推方程.首先,基于新息分析方法,分别对系统状态和虚拟状态设计局部一步预报器.然后,基于一步预报器设计状态的局部线性滤波器、多步预报器和平滑器.推导了任意两个局部状态估计误差之间的互协方差矩阵.接着,基于线性最小方差意义下的矩阵加权、对角矩阵加权和标量加权融合算法,给出相应的分布式融合状态估值器.最后,分析算法的稳定性.仿真研究验证了该算法的有效性.     

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

基于新息分析方法, 对带有色观测噪声的多重时滞系统, 提出了一种带白噪声估值器的非增广的最优滤波器. 它等价于一个带相关白噪声多重时滞系统的一步预报器. 当系统带有多个传感器时, 推导了多重时滞系统的任意两个传感器子系统之间的估计误差互协方差阵. 基于线性最小方差最优加权融合估计算法, 给出了分布式加权融合最优滤波器. 分布式融合估计比基于每个传感器的局部估计具有更高的精度. 比增广的集中式最优滤波器具有更好的可靠性, 且避免了高维计算和大存储空间. 仿真例子验证了其有效性.     

基于异步多速率观测的噪声去相关融合算法

对于异步多传感器观测数据,基于先同步,再去相关的思想,提出了一种改进的左同步提升异步观测融合算法。采用左同步法避免了右同步过程中的系统状态矩阵求逆和可能出现的非因果问题;对同步后的系统基于Cholesky分解进行噪声去相关处理,理论上分析了去相关处理前后的算法计算量;用信息滤波器进行预测估计,简化了滤波增益的计算过程。仿真结果表明：改进算法能够在不减小跟踪精度的基础上减小计算量,增强了算法的实时性。     

多传感器最优信息融合白噪声反卷积滤波器

应用现代时间序列分析方法和白噪声估计理论,基于线性最小方差意义下按标量加权最优信息融合准则,对于带白色和有色观测噪声的多传感器单通道系统,提出了分布式融合白噪声反卷积滤波器.它由局部白噪声反卷积滤波器加权构成.可统一处理融合滤波、平滑和预报问题.给出了计算局部滤波误差互协方差公式,可用于计算最优加权.同单传感器情形相比,可提高融合滤波器精度.它可应用于石油地震勘探信号处理.一个3传感器信息融合Bernou lli-Gaussian白噪声反卷积滤波器的仿真例子说明了其有效性.     

量测随机延迟下带相关乘性噪声的非线性系统分布式估计

本文提出了乘性噪声和加性噪声相关下的量测随机延迟非线性系统分布式状态估计.在所考虑系统中,相关状态被多传感器簇构成的传感器网所观测.所得理想量测被传送到远程分布式处理网,并伴随服从一阶马尔可夫过程的随机延迟.在此基础上,本文提出了分布式高斯信息滤波(distributed Gaussian-information filter,DGIF),来实现估计精度与计算时间的折中.在单处理节点/单元中,以估计误差协方差最小化为准则,设计了相应的高斯递推滤波,并实现了延迟概率的在线递推估计.进一步地,在分布式处理网中,基于非线性量测方程的统计线性回归,结合一致性算法,给出了一种分布式信息滤波形式,有效实现了分布式融合.分别在单处理单元和分布式处理网中仿真验证了所提算法的有效性.     

多传感器采样系统的分布式容错估计

研究了多传感器采样系统在发生一类典型故障情况下的分布式融合估计问题;首先,针对局部传感器,利用Kalman滤波获得的新息进行故障检测;然后在最小方差意义下发展了传感器故障在线递归估计方案;进一步将所获得的估计结果对故障传感器的测量值进行重构,并应用射影定理建立了局部传感器容错更新算法;最后基于线性最小方差融合原则给出了多传感器采样系统的分布式容错估计方案;相比于已有融合估计方法,所提方案不仅能及时检测传感器故障,并且能进一步充分利用故障传感器信息来提高估计精度;数值仿真验证了方法的有效性和优越性。     

噪声相关的一步滞后无序量测递推融合算法

因传感器网络特殊的通信方式,以及传感器节点预处理量测的时间也各有不同,常会出现源于同一目标有序的测量数据却经网络传输后无序地到达融合中心的现象,即无序量测问题.加之,现有的相关融合算法大都是在各量测数据间噪声独立情况下建立的.为此,针对一个由多个子系统组成的传感器网络无序量测系统;其中假定每个子系统均是由两个分别与融合中心同步与异步且采样率相同的传感器组成;并在考虑各传感器测量噪声相关条件下,利用顺序加权融合技术,在融合中心建立一个能实现对目标状态实时估计且在线性最小均方误差意义下最优的递推加权融合算法.理论分析与计算机仿真表明,与现有方法相比,新算法在适用范围、实时处理能力、存储量和融合估计精度等方面均有显著的优势.     

多传感器异步线性测量系统的数据融合

由于采样速率和传送数据到融合中心的通信延迟的不同,现代工业生产过程中用于对未知的常值或缓变参数进行估计的多传感器通常是异步工作的,且受到加性测量噪声的干扰。在最小二乘估计意义下,对于测量噪声互不相关的多传感器异步线性测量系统,提出了集中式和分布式递推参数估计数据融合算法,两种算法完全等价,且都是全局最优的。数值仿真实验的结果表明,通过利用多传感器的测量数据,增大了对参数测量的数据流和数据率,传感器测量参数的估计准确度得到明显改善。     

分布式视频编码中相关噪声分布的非参数估计

目的 针对分布式视频编码系统中相关噪声（CN）分布难以准确模拟的问题,提出了一种CN的非参数估计方法。方法 根据CN分布的特点,提出CN的非参数估计方法,建立了基于最优窗宽的核密度估计-均匀分布模型（KDEUDM）,比较了变换域Wyner-Ziv（TDWZ）系统中CN的参数估计法和非参数估计法所建立的噪声模型对系统性能的影响。结果 实验结果表明,非参数估计方法能较准确地模拟CN的分布,与参数估计法相比,用非参数估计法建立的噪声模型能使WZ帧编码在高码率下最高能节约10%的码率。结论 非参数估计法是TDWZ系统中有效的相关噪声估计方法。     

Distributed fusion estimation for multi-sensor asynchronous sampling systems with correlated noises

This paper is concerned with the distributed fusion estimation problem for a class of multi-sensor asynchronous sampling systems with correlated noises. The state updates uniformly and the sensors sample randomly. Based on the measurement augmentation method, the asynchronous sampling system is transformed to the synchronous sampling one. Local filter is designed by using an innovation analysis approach. Then, the filtering error cross-covariance matrix between any two local filters is derived. Finally, the optimal distributed fusion filter is proposed by using matrix-weighted fusion algorithm in the linear minimum variance sense. Simulation results show the effectiveness of the proposed algorithms.     

空间连接系统分布式递推状态估计

针对空间连接系统, 提出一种分布式递推状态估计算法, 并给出算法收敛的充分必要条件. 该分布式估计器由一系列子估计器组成, 每个子估计器只利用本地子系统和相邻子系统的输出测量值估计本地子系统的状态. 与集总式Kalman 滤波相比, 在牺牲少量估计精度的情况下, 所提出算法大幅降低了计算复杂度和数据传输压力.     

Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises

The information fusion estimation problems are investigated for multi-sensor stochastic uncertain systems with correlated noises. The stochastic uncertainties caused by correlated multiplicative noises exist in the state and observation matrices. The process noise and the observation noises are one-step auto-correlated and two-step cross-correlated, respectively. While the observation noises of different sensors are one-step cross-correlated. The optimal centralized fusion filter, predictor and smoother are proposed in the linear minimum variance sense via an innovative analysis approach. To enhance the robustness and flexibility, a distributed fusion filter is put forward, which requires the calculation of filtering error cross-covariance matrices between any two local filters. To avoid the calculation of cross-covariance matrices, another distributed fusion filter is also presented by using the covariance intersection (CI) fusion algorithm, which can reduce the computational cost. A simulation example is given to show the effectiveness of the proposed algorithms.     

Robust weighted fusion Kalman estimators for multisensor systems with multiplicative noises and uncertain‐covariances linearly correlated white noises

This paper addresses the design of robust weighted fusion Kalman estimators for a class of uncertain multisensor systems with linearly correlated white noises. The uncertainties of the systems include the same multiplicative noises perturbations both on the systems state and measurement output and the uncertain noise variances. The measurement noises and process noise are linearly correlated. By introducing two fictitious noises, the system under consideration is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case systems with the conservative upper bounds of the noise variances, the four robust weighted fusion time‐varying Kalman estimators are presented in a unified framework, which include three robust weighted state fusion estimators with matrix weights, diagonal matrix weights, scalar weights, and a modified robust covariance intersection fusion estimator. The robustness of the designed fusion estimators is proved by using the Lyapunov equation approach such that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The accuracy relations among the robust local and fused time‐varying Kalman estimators are proved. The corresponding robust local and fused steady‐state Kalman estimators are also presented, a simulation example with application to signal processing to show the effectiveness and correctness of the proposed results. Copyright © 2016 John Wiley & Sons, Ltd.     

Recursive linear optimal filter for Markovian jump linear systems with multi-step correlated noises and multiplicative random parameters

This paper presents the state estimation problem for discrete-time Markovian jump linear systems with multi-step correlated additive noises and multiplicative random parameters (termed as MCNMP). A recursive linear optimal filter for the considered MCNMP (which is abbreviated as RLMMF) is derived based on state augmentation between the original state and mode uncertainty, with the help of estimating the multi-step correlated additive noises online simultaneously. A maneuvering target tracking example under one-step and two-step correlated additive noises scenarios with different cases (i.e. Gaussian/Gaussian mixture distribution and no multiplicative noises) is simulated to validate the designed filter.     

Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises

In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis.     

带相关噪声、随机观测滞后和丢失的随机不确定系统的最优线性估值器

对带有限步相关噪声、乘性噪声、多步随机观测滞后和丢失的复杂网络化控制系统,根据相关噪声的步数,分析了噪声和状态、噪声和观测、噪声和新息、观测和新息、状态和新息之间的相关性,给出了相关阵的递推计算公式.利用射影理论,提出了线性最小方差最优线性估值器,包括滤波器、预报器和平滑器.一个网络监测环境下的三容器水箱系统的实例仿真,验证了算法的有效性.     

Distributed fusion filter for multi-sensor systems with finite-step correlated noises

This paper addresses the distributed fusion filtering problem for multi-sensor systems with finite-step correlated noises. The process noise and observation noises of different sensors are finite-step auto- and cross-correlated, respectively. Based on the optimal local filtering algorithms that we presented before, the filtering error cross-covariance matrices between any two local filters are derived based on an innovation analysis approach. A distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. Finally, the proposed algorithms are extended to systems with random parameter matrices. Two simulation examples are given to show the effectiveness of the proposed algorithms.