集中式与分布式鲁棒状态融合估计

研究不确定多传感器系统的鲁棒估计问题是多传感器融合估计理论的一个重要研究方向.本文以鲁棒滤波理论为基础,给出了不确定多传感器系统的多胞型描述模型,并利用LMI方法给出集中式鲁棒状态融合估计问题的解,证明了将集中式鲁棒融合估计转化为相同估计性能的分布式融合估计算法的条件.最后给出了分布式不确定多传感器系统的状态融合估计的一个算例.     

带不确定方差乘性和加性噪声系统鲁棒加权 融合稳态Kalman估值器

本文研究带不确定方差乘性和加性噪声和带状态相依及噪声相依乘性噪声的多传感器系统鲁棒加权融合估计问题.通过引入虚拟噪声补偿乘性噪声的不确定性,将原系统化为带确定参数和不确定加性噪声方差的系统,进而利用Lyapunov方程方法提出在统一框架下的按对角阵加权融合极大极小鲁棒稳态Kalman估值器(预报器、滤波器和平滑器),其中基于预报器设计滤波器和平滑器,并给出每个融合器的实际估值误差方差的最小上界.证明了融合器的鲁棒精度高于每个局部估值器的鲁棒精度.应用于不间断电源(uninterruptible power system,UPS)系统鲁棒融合滤波的仿真例子说明了所提结果的正确性和有效性.     

带不确定参数和噪声方差的鲁棒观测融合Kalman滤波器

对带不确定参数和噪声方差的多传感器定常系统,引入虚拟白噪声补偿不确定参数,可将其转化为带已知参数和不确定噪声方差系统.应用极大极小鲁棒估值原理和加权最小二乘法,基于带噪声方差保守上界的最坏情形保守系统,提出了鲁棒加权观测融合Kalman滤波器,并证明了它与集中式融合鲁棒Kalman滤波器是等价的,且融合器的鲁棒精度高于每个局部滤波器鲁棒精度.一个Monte-Carlo仿真例子说明了如何寻求不确定参数的鲁棒域和如何搜索保守性较小的虚拟噪声方差上界.     

连续切换LPV系统的鲁棒H∞滤波

探究连续切换线性参数变化(LPV)系统的鲁棒H∞滤波问题.对于整个参数变化空间,传统方法是设计单一LPV滤波器,具有较大的保守性.为此,利用多参数依赖Lyapunov函数设计了切换LPV系统的多参数依赖鲁棒H∞滤波器,以降低设计的保守性.考虑了平均驻留时间切换逻辑,所设计的鲁棒H∞滤波器能够确保滤波误差系统指数稳定且具有一定的H∞扰动抑制水平.数值仿真实例验证了所提出方法的有效性.     

广义时变系统的鲁棒H2/H∞滤波

研究一类广义时变系统的鲁棒H2/H∞滤波器的设计问题。 利用具有参数依赖的Lyapunov函数,结合线性矩阵不等式分别给出了使滤波误差系统容许且满足H∞性能约束的充分条件、使滤波误差系统容许且满足H2性能约束的充分条件以及使滤波误差系统容许且满足H2/H∞性能指标的充分条件。 基于该具有参数依赖Lyapunov函数的鲁棒H2/H∞性能准则推导出鲁棒H2/H∞滤波器存在的充分条件,并将滤波器的设计问题转化为线性矩阵不等式约束优化问题。 最后通过数值算例表明该方法的有效性。     

分簇传感网络系统两级鲁棒观测融合Kalman滤波器

研究了分簇传感网络分布式融合Kalman滤波器.根据最邻近原则将传感网络分成簇,每簇由传感节点和簇首组成.应用极大极小鲁棒估计原理,基于带噪声方差最大保守上界的最坏保守系统,对带不确定性噪声方差的分簇传感网络系统提出了两级鲁棒观测融合Kalman滤波器.当传感器数量非常多的时候它可以明显减小通信负担.在鲁棒性分析中利用Lyapunov方程方法证明了局部和融合Kalman滤波器的鲁棒性.提出了鲁棒精度的概念,并证明了局部和融合鲁棒Kalman滤波器之间的鲁棒精度关系.证明了两级加权观测融合器的鲁棒精度等价于相应的全局集中式鲁棒融合器的鲁棒精度,并且高于每个局部观测融合器的鲁棒精度.一个仿真例子说明上述结果的准确性.     

一类高速采样不确定系统的鲁棒H∞滤波器设计

研究含有多面体参数摄动Delta算子系统的鲁棒H∞滤波问题．基于Delta算子系统有界实引理，提出了新的参数依赖型鲁棒H∞性能准则．利用该性能准则，采用线性矩阵不等式技术推导了此类系统全阶鲁棒H∞滤波器存在的充分条件，并通过求解一个凸优化问题来设计滤波器．与基于二次稳定的滤波方案相比，该方法具有较小的保守性．最后以数值示例验证了所提出方法的可行性．     

不确定连续系统的鲁棒H2/H∞滤波

研究凸多面体不确定连续系统的鲁棒H2／H∞滤波问题．为降低设计的保守性．提出一种新的具有参数依赖Lyapunov函数的鲁棒H2／H∞性能准则．基于该性能准则，推导出鲁棒H2／H∞滤波器存在的充分条件．并将滤波器设计问题转化为具有线性矩阵不等式（LMI）约束的参数优化问题．     

离散时间不确定系统的混合l1/H∞滤波

研究离散时间不确定线性系统的混合l1/H∞滤波器设计问题,目的是找到一个稳定的线性滤波器,使滤波误差系统在不同的滤波通道内具有不同的性能指标.利用参数依赖Lyapunov函数法,推导出新的鲁棒l1/H∞性能准则.基于该性能准则推导了全阶和降阶鲁棒l1/H∞滤波器存在的充分条件,并将滤波器的设计问题转化为具有线性矩阵不等式约束的凸优化求解问题.     

不确定多传感器系统鲁棒观测融合Kalman 预报器

对于带不确定模型参数和噪声方差的线性离散时不变多传感器系统, 用虚拟噪声补偿不确定参数, 系统转化为仅带噪声方差不确定性的多传感器系统. 用加权最小二乘法和极大极小鲁棒估计准则, 基于带噪声方差保守上界的最坏情形保守系统, 提出一种鲁棒加权观测融合稳态Kalman 预报器, 并应用Lyapunov 方程方法证明了它的鲁棒性, 同时给出了与鲁棒局部和集中式融合Kalman 预报器的精度比较. 最后通过一个仿真例子说明了如何搜索参数扰动的鲁棒域, 并验证了所提出的理论结果的正确性和有效性.

     

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.     

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.     

带未知模型参数和衰减观测率系统自校正分布式融合估计

研究了带未知模型参数和衰减观测率多传感器线性离散随机系统的信息融合估计问题.在模型参数和衰减观测率未知的情形下,应用递推增广最小二乘(Recursive extend least squares,RELS)算法和加权融合估计算法提出了分布式融合未知模型参数辨识器;应用相关函数对描述衰减观测现象的随机变量的数学期望和方差...     

Distributed multiple model estimation for jump Markov linear systems with missing measurements

This paper is concerned with distributed multiple model estimation for jump Markov linear systems with missing measurements over a sensor network. Two independent Markov chains are used to describe the switching of dynamic models and the missing of measurements, respectively. Under the assumption that each sensor can only communicate with its neighbours, a distributed filter is developed by applying the basic interacting multiple model (IMM) approach in the Bayesian estimation framework. To circumvent the difficulty of exponentially growing filters by exchanging local measurements between neighbouring sensors, the mode-conditioned estimates are exchanged instead of local measurements and the covariance intersection method is adopted to fuse mode-conditioned estimates. A multi-sensor manoeuvering target tracking example is provided to verify the effectiveness of the proposed filter.     

Multi-sensor distributed fusion filtering for networked systems with different delay and loss rates

This paper mainly focuses on the multi-sensor distributed fusion estimation problem for networked systems with time delays and packet losses. Measurements of individual sensors are transmitted to local processors over different communication channels with different random delay and packet loss rates. Several groups of Bernoulli distributed random variables are employed to depict the phenomena of different time delays and packet losses. Based on received measurements of individual sensors, local processors produce local estimates that have been developed in a new recent literature. Then local estimates are transmitted to the fusion center over a perfect connection, where a distributed fusion filter is obtained by using the well-known matrix-weighted fusion estimation algorithm in the linear minimum variance sense. The filtering error cross-covariance matrices between any two local filters are derived. The steady-state property of the proposed distributed fusion filter is analyzed. A simulation example verifies the effectiveness of the algorithm.     

噪声方差未知情况下多传感器航迹融合

在多传感器信息融合中,已有的航迹融合算法都是在噪声方差已知情况下基于最优的卡尔曼滤波算法的,而实际应用中噪声方差往往是未知的.针对上述问题,基于扩展记忆因子递推最小平方(EFRLS)估计的滤波方程,研究了噪声方差未知情况下集中式、分布式、混合式多传感器航迹融合方法.并对三种航迹融合算法的跟踪性能和卡尔曼滤波融合算法的性能进行了仿真比较.由于多级式多传感器的航迹融合方法可由本文的方法直接推广,所以只需研究两级的情况就可.     

粒子滤波理论、方法及其在多目标跟踪中的应用

本文梳理了粒子滤波理论基本内容、发展脉络和最新研究进展, 特别是对其在多目标跟踪应用中的一系列难点问题与主流解决思路进行了详细分析和报道. 常规粒子滤波研究重点主要围绕重要性采样函数、计算效率、权值退化/样本匮乏和复杂系统建模展开. 作为一类复杂估计问题,多目标跟踪一方面需要准确的目标新生/消亡与演变、虚警/漏检等建模技术, 另一方面需要多传感器信息融合、航迹管理等复杂决策方法.暨有限集统计学应用于多目标跟踪后,粒子 滤波进入一个新的发展阶段---随机集粒子滤波.基于不同的背景假设,可以构建不同近似形式的随机集贝 叶斯滤波器并采用粒子滤波实现.但机动目标、未知场景、多目标航迹管理以及跟踪性能评价等仍是多 目标粒子滤波的研究难点和重点.     

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.     

Robust energy-to-peak filter design for stochastic time-delay systems

This paper considers the robust energy-to-peak filtering problem for uncertain stochastic time-delay systems. The stochastic uncertainties appear in both the dynamic and the measurement equations and the state delay is assumed to be time-varying. Attention is focused on the design of full-order and reduced-order filters guaranteeing a prescribed energy-to-peak performance for the filtering error system. Sufficient conditions are formulated in terms of linear matrix inequalities (LMIs), and the corresponding filter design is cast into a convex optimization problem which can be efficiently handled by using standard numerical algorithms. In addition, the results obtained are further extended to more general cases where the system matrices also contain uncertain parameters. The most frequently used ways of dealing with parameter uncertainties, including polytopic and norm-bounded characterizations, have been taken into consideration, with convex optimization problems obtained for the design of desired robust energy-to-peak filters.