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.     

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.     

具有一步随机滞后和多丢包的网络系统的最优线性估计

研究了具有随机时滞和丢包的网络系统的最优线性估计问题.本文通过两个满足 Bernoulli分布的随机变量来描述网络数据传输中可能存在的一步随机滞后和多丢包现象. 并基于新息分析方法,提出了线性最小方差下的最优线性状态滤波器、预报器和平滑器. 它们通过解一个Riccati方程和一个Lyapunov方程得到.最后,给出了稳态估值器存在的一个充分条件. 并通过仿真例子验证其有效性.     

具有有限连续丢包网络控制系统的最优线性估值器

本文研究了观测数据和控制输入数据传输具有有限连续丢包的线性离散随机系统的最优估计问题.利用两个满足Bernoulli分布的随机变量来分别描述从传感器到估值器和从控制器到执行器之间的数据丢包现象.通过引入两组新的变量,将原系统转化为一个带有随机参数的系统.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.最后研究了稳态线性估值器,并给出了稳态存在的一个充分条件.仿真例子验证了算法的有效性.     

带相关噪声异步采样系统的分布式最优线性递推融合估计

对带相关噪声的异步均匀采样线性离散系统, 研究了分布式最优线性递推融合预报和滤波问题. 通过引入 满足伯努利分布的随机变量将系统同步化, 给出了局部Kalman预报器和滤波器. 分别推导了局部估值间的互协方 差阵、分布式最优线性融合估值与局部估值间的互协方差阵. 提出了分布式最优线性递推融合预报器和滤波器. 与 局部估值按矩阵加权的分布式融合估计算法相比, 所提出的算法具有更高的估计精度, 但与集中式融合相比有精度 损失. 为了进一步提高估计精度, 又提出了带反馈的分布式最优线性递推融合预报器和滤波器, 证明了带反馈的融 合估计与集中式融合估计具有相同的精度. 仿真例子验证了所提算法的有效性.     

网络化最优预测状态估计

当网络诱导时延和数据包丢失确定可知时,提出了一种网络化最优预测状态估计器设计方法,能够补偿网络诱导时延和数据包丢失对估计性能的影响,理论分析表明了随着网络诱导时延或数据包丢失的增加,该估计器在获得明显补偿效果的同时预测估计偏差略微递增,并给出了估计系统的稳定性条件,最后通过仿真和实验验证了所提出方法的有效性和理论分析的正确性.     

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.     

具有传感器增益退化、随机时延和丢包的分布式融合估计器

研究了具有传感器增益退化、模型不确定性、数据传输时延和丢包的网络化多传感器分布式融合估计问题,模型的不确定性描述为系统矩阵受到随机扰动,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,随机时延和丢包现象存在于局部最优状态估计向融合中心传输的过程中.首先,设计了一种局部最优无偏估计器,然后将传输时延描述为随机过程,并在融合中心端建立符合存储规则的时延-丢包模型,利用最优线性无偏估计方法,导出最小方差意义下的分布式融合估计器.最后,通过算例仿真证明所设计融合估计器的有效性.     

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.     

基于线性编码的随机时延和丢包下的状态估计

关于具有数据随机传输时延和丢包的网络化状态估计问题,针对多个测量数据包同时到达远程处理中心的情况,为减轻计算负担,提出了一种线性编码方法对多个测量值进行线性重组进而用以推导估计器,并给出估计器稳定的充分条件.在最小方差原则下通过改变编码参数组合来改变估计器精度,通过算例仿真验证所提估计器的有效性.结果表明,上述估计器能...     

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.     

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.     

随机系统中能容忍连续丢包和测量时延的卡尔曼滤波

通过转换原线性系统到能容忍连续丢包和测量时延的随机参数系统,推导了一个最优线性滤波器.给出一个仿真例子,比较已存在的结果,仿真结果表明被提出的线性滤波器有优越的性能.然而,该滤波器不能应用于非线性系统.从应用角度,为非线性系统提出了一个增强型的滤波器.而且,该增强型的滤波器能成功地应用于不可靠的无线传感器网络场景来跟踪移动目标.这些滤波器只依靠测量值的达到概率,而不需要知道某一时刻测量是否接收.仿真说明了被提出的增强型滤波器不仅能改善实时目标跟踪的鲁棒性,而且比标准的扩展卡尔曼滤波器能够提供更精确的估计.     

Robust fractional order singular Kalman filter

In this article, the state estimation problem of linear fractional order singular (FOS) systems subject to matrix uncertainties is investigated where a recursive robust algorithm is derived. Considering an uncertain discrete-time linear FOS system with added process and measurement noises, we aim to design a robust Kalman-type state estimation algorithm based on an optimal data fitting approach with a given sequence of observations. As a substitute for the stochastic formulation, this general filter is obtained by minimizing a completely deterministic regularized residual norm in its worst-possible form at each step over admissible uncertainties. Analysis of the algorithm shows that not only does the proposed robust filter cover the traditional robust Kalman filters (KFs), but it also represents an extension of the nominal fractional singular KF (FSKF) when the system is not subject to uncertainties. Furthermore, besides giving a sufficient condition for the existence of the robust filter, we derive conditions for the asymptotic properties of the filter, where we demonstrate that the filter and the Riccati equation are stable and converge when an equivalent system is detectable and stabilizable. A numerical example is included to demonstrate the performance of the introduced filter.     

New distributed fusion filtering algorithm based on covariances over sensor networks with random packet dropouts

This paper studies the distributed fusion estimation problem from multisensor measured outputs perturbed by correlated noises and uncertainties modelled by random parameter matrices. Each sensor transmits its outputs to a local processor over a packet-erasure channel and, consequently, random losses may occur during transmission. Different white sequences of Bernoulli variables are introduced to model the transmission losses. For the estimation, each lost output is replaced by its estimator based on the information received previously, and only the covariances of the processes involved are used, without requiring the signal evolution model. First, a recursive algorithm for the local least-squares filters is derived by using an innovation approach. Then, the cross-correlation matrices between any two local filters is obtained. Finally, the distributed fusion filter weighted by matrices is obtained from the local filters by applying the least-squares criterion. The performance of the estimators and the influence of both sensor uncertainties and transmission losses on the estimation accuracy are analysed in a numerical example.     

Linear state estimators for non-linear stochastic systems with noisy non-linear observations

The design of linear filters is considered for reconstructing the state of a class of discrete-time non-linear stochastic systems using noise-corrupted measurements. It is shown that for systems with mean-square stable dynamics, it is always possible to guarantee stable estimation schemes. This result is used to prove that a mean–square optimal one-step predictor has stable error dynamics and also to generate other stable predictors.     

一类具有Markov跳跃参数的不确定混合线性时滞系统鲁棒稳定性研究

讨论了一类具有Markov跳跃参数的不确定混合线性时滞系统的鲁棒稳定性问题．分别给出了非匹配条件下不确定部分范数上界已知时使混合线性系统以概率1渐近稳定的充分条件,和匹配条件下不确定部分范数上界未知时同样可以实现混合系统以概率1渐近稳定的鲁棒自适应控制设计方案．文章研究结果表明,此控制方案对混合线性时滞系统的不确定部分是有效的．     

Covariance-based fusion filtering for networked systems with random transmission delays and non-consecutive losses

The distributed and centralized fusion filtering problems for multi-sensor networked systems with transmission random one-step delays and non-consecutive packet losses are addressed. The signal evolution model is not required, as only covariance information is used. The measurements of individual sensors, subject to uncertainties modeled by random matrices and correlated noises, are transmitted to local processors through different communication channels and, due to random transmission failures, some of the data packets may be delayed or even definitely lost. The random transmission delays and non-consecutive packet losses are modeled by sequences of Bernoulli variables with different probabilities. By an innovation approach, local least squares linear filtering estimators are obtained by recursive algorithms; the distributed fusion framework is then used to obtain the optimal matrix-weighted combination of the local filters, using the mean squared error as optimality criterion. Also, a recursive least squares linear estimation algorithm is designed within the centralized fusion framework.     

Event-triggered multi-rate fusion estimation for uncertain system with stochastic nonlinearities and colored measurement noises

This paper is concerned with the event-triggered robust fusion estimation problem for uncertain multi-rate sampled-data systems with stochastic nonlinearities and the colored measurement noises. Due to the effects of stochastic nonlinearities and parameter uncertainties, a new augmentation approach is proposed by which the multi-rate sampled-data system under consideration is transformed into the single-rate system. In order to eliminate the effect of the colored measurement noises, a measurement model with uncorrected noises is established. Based on the measurement model established, a set of local event-triggered filters is constructed and the upper bounds of the local filtering error covariances at each sampling instant are obtained. By using the Lagrange multiplier method, the local filter parameters are designed such that the upper bound obtained is minimum. For the local state estimates, a new fusion estimation scheme is proposed with the help of covariance intersection (CI) method and the consistency of the proposed CI-based fusion estimation scheme is shown. Finally, an illustrative example is presented to verify the effectiveness of the fusion estimation scheme proposed.