具有传感器增益退化、传输时延和丢包的离线状态估计器

研究了具有传感器增益退化、数据传输时延和丢包的网络化状态估计问题, 传感器增益退化现象通过统计特性已知的随机变量来描述, 数据包时延和丢失发生于传感器量测输出向远程处理中心传送过程中, 将各时延的发生描述为随机过程, 在远程处理中心端建立只存储最新时刻数据包的时延-丢包模型, 考虑到利用每一时刻实时的时延值和丢包情况, 设计了一种离线的无偏估计器, 推导出最小方差原则下的离线最优估计器增益.最后, 通过算例仿真验证所设计离线状态估计器的有效性.     

具有传感器增益退化的不确定系统融合估计器

研究具有传感器增益退化、模型不确定性的多传感器融合估计问题, 其中传感器增益退化现象描述为统计特性已知的随机变量, 模型的不确定性描述为系统矩阵受到随机扰动. 设计一种局部无偏估计器结构, 并建立以局部估计器增益为决策变量、以有限时域下融合估计误差为代价函数的优化问题. 在给出标量融合权重时, 考虑到求得最优的局部估计器增益的解析形式较为困难, 通过最小化代价函数的上界得到一组次优的局部估计器增益. 最后通过算例仿真表明了所设计融合估计器的有效性.

     

带未知通信干扰和丢包补偿的多传感器网络化不确定系统的分布式融合滤波

研究了带有未知通信干扰、观测丢失和乘性噪声不确定性的多传感器网络化系统的状态估计问题.通过白色乘性噪声描述系统状态和观测中的随机不确定性,采用一组服从Bernoulli分布的随机变量描述网络传输过程中存在的观测丢失现象,且数据传输中存在未知的网络通信干扰.当发生丢包时,以当前丢失观测的预报值进行补偿.对每个单传感器子系统,应用线性无偏最小方差估计准则设计了不依赖于未知通信干扰的最优线性滤波器.推导了任两个局部滤波误差之间的互协方差阵.进而,应用矩阵加权融合估计算法给出了分布式融合状态滤波器.仿真例子验证了算法的有效性.     

具有Markovian时延与丢包的离散系统的状态估计

网络化控制系统中经常会因网络带宽有限而导致数据包在网络中传输时产生时延甚至丢失.本文主要研究具有Markovian时延与丢包的离散线性系统的状态估计问题.通过在估计器端设置适当长度的缓存器,把具有多状态Markovian时延与丢包的离散定常系统建模成数据包到达过程为两状态Markovian模型的离散时变系统,并基于跳跃线性估计器的思想提出了一类特殊的估计器,即限定接收历史估计器 (FRHE).在最大时延已知时,给出了可选增益的最优RHE设计策略.该策略虽然是次优,却能提供简便的计算.通过与时变Kalman估计器 (TVKE)的仿真对比,表明所提策略的有效性.     

带多丢包和滞后随机不确定系统的最优线性估计

研究了带多丢包和滞后网络化随机不确定系统的最优线性估计问题. 通过白色乘性噪声来描述系统参数的随机不确定性. 通过一组满足Bernoulli分布的随机变量来描述数据传输过程中发生的丢包和滞后现象. 应用新息分析方法, 设计了线性最小方差意义下的最优线性估值器, 包括滤波器, 预报器和平滑器. 给出了稳态估值器存在的一个充分条件. 仿真例子验证了其有效性.     

通信受限下网络化多传感器系统的Kalman融合估计

研究了一类通信受限下网络化多传感器系统的 Kalman 融合估计问题, 其中通信受限 是指系统在一个采样周期内只允许有限个传感器与融合中心通信. 首先, 提出了一种周期性分组传输的通信策略, 并将每组传感器所对应的局部估计系统描述成一个离散周期子系统模型. 其次, 每个子系统根据最新测量信息的更新时刻, 选择相应的 Kalman 估计器 (滤波器或预报器), 从而得到各子系统在每一时刻的一个局部最优估计, 再通过矩阵加权线性最小方差最优融合准则得到最优融合估计,并给出了Kalman融合估计器的设计方法. 最后, 通过一个目标跟踪例子验证所提方法的有效性.     

能量和带宽受限下的分布式一致性融合估计器

针对网络化多传感器分布式估计中传感器能量和通信网络带宽约束问题,提出一种基于降低发送频率和数据压缩降维的分布式一致性融合估计算法.为了满足通信网络带宽要求,各传感器节点直接选取局部估计信号的部分分量进行传输;与此同时,各节点随机间歇式发送数据包到其他节点来节省能量.在给定一致性权重下,建立以一致性估计器增益为决策变量,以所有传感器节点有限时域下状态融合估计误差协方差矩阵的迹的和为代价函数的优化问题,基于Lyapunov稳定性理论给出使得融合估计误差在无噪声时渐近稳定的一致性估计器增益存在的充分条件,并通过最小化代价函数的上界得到一组次优的一致性估计器增益值.最后,通过算例仿真验证算法的有效性.     

离散随机奇异系统的最优融合全阶状态估值器

研究带多传感器和相关观测噪声的离散随机奇异系统的分布式融合状态估计问题.核心思想是将带多传感器的随机奇异系统转化为一个等价的非奇异系统组.在得到局部非奇异系统的状态估计后,利用线性最小方差意义下的最优加权融合算法,得到原系统的全阶最优融合滤波器和平滑器.仿真算例表明,融合估值器优于每个局部估值器.     

具有随机多丢包的ARMA信号最优线性估计

研究网络环境下具有随机丢包的自回归滑动平均(ARMA)信号的估计问题,其中丢包现象通过一个满足Bernoulli分布的随机变量描述.通过ARMA模型与状态空间模型的转化,将具有丢包的ARMA信号估计问题转化为具有丢包的状态空间模型的状态和白噪声估计问题.利用射影理论分别给出线性最小方差最优线性状态估值器和白噪声估值器,进而获得ARMA信号估值器.仿真结果表明,当存在数据丢失时,所提出的算法与以往基于完整数据的最优估计算法相比具有最优性和有效性.     

受有线无线异构网络诱导延时和数据丢包约束的分布式网络化H2/H∞滤波研究

针对分布式有线无线异构网络化滤波系统中部署在不同地理空间的多传感器通过无线网络与每个局部融合中心通信, 然后测量数据被传到网关并进行协议转换后通过有线网络传输到对应的分布式滤波器, 会导致数据传输出现分布式有线无线网络诱导延时和数据丢包, 使得H₂/H_∞滤波更加困难的问题, 本文首先采用有向图描述分布式传感器节点的通信拓扑, 然后运用Markov链和伯努利分布分别刻画分布式有线无线网络诱导延时和数据丢包特性, 进而建立了融合分布式滤波器参数、有线无线异构网络通信约束的普适滤波误差动态系统综合模型.理论上证明了在分布式有线无线异构网络通信约束下所设计的滤波器使得滤波误差动态系统随机稳定且满足给定的H₂/H_∞性能指标, 并建立了系统随机稳定性、分布式滤波器参数及最长有线无线网络诱导延时和数据丢包之间的关系.最后, 仿真实例验证了本文所提方法是可行且有效.     

State estimation for stochastic time‐varying multisensor systems with multiplicative noises: Centralized and decentralized data fusion

In this paper, the state estimation problems, including filtering and one‐step prediction, are solved for uncertain stochastic time‐varying multisensor systems by using centralized and decentralized data fusion methods. Uncertainties are considered in all parts of the state space model as multiplicative noises. For the first time, both centralized and decentralized estimators are designed based on the regularized least‐squares method. To design the proposed centralized fusion estimator, observation equations are first rewritten as a stacked observation. Then, an optimal estimator is obtained from a regularized least‐squares problem. In addition, for decentralized data fusion, first, optimal local estimators are designed, and then fusion rule is achieved by solving a least‐squares problem. Two recursive equations are also obtained to compute the unknown covariance matrices of the filtering and prediction errors. Finally, a three‐sensor target‐tracking system is employed to demonstrate the effectiveness and performance of the proposed estimation approaches.     

Distributed fusion filters from uncertain measured outputs in sensor networks with random packet losses

This paper addresses the distributed fusion filtering problem for discrete-time random signals from measured outputs perturbed by random parameter matrices and correlated additive noises. These measurements are obtained by a sensor network with a given topology, where random packet dropouts occur during the data transmission through the different network communication channels. The distributed fusion estimation is accomplished in two phases. Firstly, by an innovation approach and using the last observation that successfully arrived if a packet is lost, a preliminary distributed least-squares estimator is designed at each sensor node using its own measurements and those from its neighbors. Secondly, every sensor collects the preliminary filters that are successfully received from its neighbors and fuses this information with its own one to generate the least-squares linear matrix-weighted distributed fusion estimator. The accuracy of the proposed estimators, which is measured by the estimation error covariances, is examined by a numerical simulation example.     

丢包和量化约束下的不确定系统分布式滚动时域估计

研究了数据丢包和量化约束下的随机不确定系统分布式状态估计问题.将丢包现象描述为随机Bernoulli序列,采用预测补偿机制对数据丢包进行补偿,将量化引入的误差转化为观测方程中的不确定参数,将系统的模型不确定性描述为系数矩阵受到随机扰动;利用固定时域内的所有观测值构造代价函数,将状态估计问题建模为带不确定参数的鲁棒最小二...     

State estimation for complex systems with randomly occurring nonlinearities and randomly missing measurements

This paper is concerned with the state estimation problem for the complex networked systems with randomly occurring nonlinearities and randomly missing measurements. The nonlinearities are included to describe the phenomena of nonlinear disturbances which exist in the network and may occur in a probabilistic way. Considering the fact that probabilistic data missing may occur in the process of information transmission, we introduce the randomly data missing into the sensor measurements. The aim of this paper is to design a state estimator to estimate the true states of the considered complex network through the available output measurements. By using a Lyapunov functional and some stochastic analysis techniques, sufficient criteria are obtained in the form of linear matrix inequalities under which the estimation error dynamics is globally asymptotically stable in the mean square. Furthermore, the state estimator gain is also obtained. Finally, a numerical example is employed to illustrate the effectiveness of the proposed state estimation conditions.     

Optimal filtering for networked systems with stochastic sensor gain degradation

In this paper, the optimal filtering problem is investigated for a class of networked systems in the presence of stochastic sensor gain degradations. The degradations are described by sequences of random variables with known statistics. A new measurement model is put forward to account for sensor gain degradations, network-induced time delays as well as network-induced data dropouts. Based on the proposed new model, an optimal unbiased filter is designed that minimizes the filtering error variance at each time-step. The developed filtering algorithm is recursive and therefore suitable for online application. Moreover, both currently and previously received signals are utilized to estimate the current state in order to achieve a better accuracy. A numerical simulation is exploited to illustrate the effectiveness of the proposed algorithm.     

Gaussian-consensus filter for nonlinear systems with randomly delayed measurements in sensor networks

This paper presents the decentralized state estimation problem of discrete-time nonlinear systems with randomly delayed measurements in sensor networks. In this problem, measurement data from the sensor network is sent to a remote processing network via data transmission network, with random measurement delays obeying a Markov chain. Here, we present the Gaussian-consensus filter (GCF) to pursue a tradeoff between estimate accuracy and computing time. It includes a novel Gaussian approximated filter with estimated delay probability (GEDPF) online in the sense of minimizing the estimate error covariance in each local processing unit (PU), and a consensus strategy among PUs in processing network to give a fast decentralized fusion. A numerical example with different measurement delays is simulated to validate the proposed method.