粒子滤波的噪声相关下单步延迟无序量测更新算法

在目标跟踪系统中,因通信延迟等原因会出现传感器量测无序地到达融合中心的现象,将这些量测称为无序量测(OOSM).针对过程噪声、量测噪声相关的非线性系统中出现的无序量测问题,在现有算法基础上,提出了一种可处理单步延迟无序量测的新算法.在前向预测滤波框架下,对系统方程去相关化,并利用粒子滤波(PF)进行状态估计.仿真结果验证了算法的有效性.     

考虑随机量测时滞和同步相关噪声的改进高斯滤波算法

经典高斯滤波算法存在量测信息实时获取,以及过程噪声和量测噪声相互独立的假设条件.然而,在工程实际应用中该假设条件有时难以满足.本文针对一类具有随机量测时滞和同步相关噪声的高斯系统的状态估计问题,设计了一种高斯滤波框架形式的最优估计算法,并给出了所设计算法的三阶球径容积法则的次优实现形式-考虑随机量测时滞和同步相关噪声的容积卡尔曼滤波器(CKF–RDSCN).其借助Bernoulli随机序列,来描述系统中可能存在的量测时滞现象,并利用高斯条件分布性质来解决噪声相关问题,在此基础上构建所提出的最优估计算法.仿真结果表明,相比于扩展卡尔曼滤波(EKF),无迹卡尔曼滤波(UKF)以及容积卡尔曼滤波(CKF),在含有随机量测时滞和噪声同步相关的状态估计问题中,CKF–RDSCN具有更高的精度和更好的数值稳定性.     

基于IMM-PF的分布式估计融合算法

针对基于扩展卡尔曼滤波的估计融合算法存在线性化误差,且受高斯噪声假设限制的问题,提出一种基于交互式多模型粒子滤波(IMM-PF)的分布式多传感器估计融合算法.各传感器节点采用IMM-PF算法,以便在非线性、非高斯条件下稳健地跟踪机动目标;融合中心则采用基于粒子滤波(PF)的分布式融合方法进行全局估计融合.该算法适用于非线性、非高斯条件下的多传感器状态估计.仿真结果表明,该算法能够提高多传感器系统状态估计的精度.     

有色量测噪声下的HCKF及其应用

针对高阶容积卡尔曼滤波（HCKF）算法在有色量测噪声条件下滤波精度下降的问题,提出了有色量测噪声下的HCKF算法。通过一阶马尔科夫模型将有色量测噪声进行白化,将带有色量测噪声的非线性离散随机系统转化为白噪声下的非线性时滞系统,并给出高斯域内针对非线性时滞系统的贝叶斯滤波框架。利用高阶容积准则对该滤波框架进行近似计算,进而得到有色量测噪声下的HCKF算法。将所提算法应用到机动目标跟踪系统中,仿真实验结果表明,量测噪声为白噪声时,所提算法与标准HCKF算法具有相同的估计性能;在量测噪声为有色噪声时,所提算法相比于标准HCKF具有更优的估计精度和鲁棒性。     

基于IMM-PF的分布式估计融合算法

针对基于扩展卡尔曼滤波的估计融合算法存在线性化误差,且受高斯噪声假设限制的问题,提出一种基于交互式多模型粒子滤波(IMM-PF)的分布式多传感器估计融合算法.各传感器节点采用IMM-PF算法,以便在非线性,非高斯条件下稳健地跟踪机动目标;融合中心则采用基于粒子滤波(PF)的分布式融合方法进行全局估计融合.该算法适用于非线性,非高斯条件下的多传感器状态估计.仿真结果表明,该算法能够提高多传感器系统状态估计的精度.

     

带有色量测噪声的非线性系统 Unscented 卡尔曼滤波器

传统Unscented卡尔曼滤波器(Unscented Kalman filter, UKF)要求噪声必须为高斯白噪声, 无法解 决带有色噪声的非线性系统滤波问题. 为此, 本文提出了一种带有色量测噪声的UKF滤 波新算法. 首先,基于量测信息增广和最小方差估计, 推导出一类带有色量测噪声的非 线性离散系统状态的最优滤波框架, 接着采用Unscented变换(Unscented transformation, UT)来计算最优框架中的 非线性状态后验均值和协方差, 进而得到有色量测噪声下UKF滤波递推公式. 所设 计的UKF新方法能有效地解决传统UKF在量测噪声有色情况下非线性滤波失效的问题, 数 值仿真实例验证了其可行性和有效性.     

基于MCC的鲁棒高阶CKF在组合导航中的应用

针对高阶容积卡尔曼滤波器在非高斯噪声情况下滤波精度下降的问题,提出了一种新的基于Maximum Correntropy Criterion（MCC）的鲁棒高阶容积卡尔曼滤波算法。考虑到高阶容积规则可以较好地解决非线性问题,在高阶容积滤波的基础上,结合统计线性回归模型对量测更新过程进行重构,利用MCC估计算法实现状态的量测更新,同时解决了系统的非线性和非高斯问题。将所提算法应用到SINS/GPS组合导航系统中,仿真结果表明,核宽的选取对算法的滤波性能有较大的影响,在高斯混合噪声条件下,所提算法相比传统高阶容积卡尔曼滤波算法具有更强的鲁棒性和更高的滤波精度。     

基于M估计的非线性鲁棒检测卡尔曼滤波算法

针对传统鲁棒非线性滤波在观测噪声为非高斯强干扰噪声情况下,滤波性能下降的问题,提出一种利用卡方检测法预判断的非线性鲁棒检测滤波算法。该算法通过卡方检测设置门限,剔除突变野值,利用M估计修正量测更新。仿真实验对比了几种典型非线性滤波方法在不同观测噪声环境下的性能。所提算法在非高斯强干扰噪声情况下,比传统鲁棒滤波算法估计精度平均提高了25.5%;估计方差平均减少了18.3%。实验结果表明:所提算法可以抑制观测量非高斯强干扰噪声的影响,提高滤波精度及稳定性。     

基于高斯似然近似的自适应球面径向积分滤波算法

针对量测噪声较小的环境下传统滤波算法容易出现偏差增大的实际问题, 基于高斯近似原理, 提出一种基于高斯似然近似的球面径向积分滤波(SRGLAF) 算法. 为进一步解决量测未知环境下的状态估计问题, 充分结合CKF 等确定性采样型滤波算法和SRGLAF 的优势, 设计一种基于高斯似然近似的自适应球面径向积分滤波(ASRGLAF) 算法. 仿真结果表明: SRGLAF 能够提高量测噪声较小环境下的估计精度, 而在量测噪声未知环境中, ASRGLAF 能够有效地进行状态估计, 具有明显的滤波优势.

     

具有形状信息的多传感器群目标跟踪算法

针对多传感器环境下具有形状信息的扩展/群目标跟踪问题,提出了两种融合算法,即高斯逆韦氏并行PHD滤波算法和高斯逆韦氏序贯PHD滤波算法。新算法分别结合并行滤波和序贯滤波算法思想,能够对扩展/群目标的质心状态进行跟踪,对形状进行有效估计。高斯逆韦氏并行PHD滤波算法将各个传感器产生的量测集合并到一个量测集中,统一对量测集进行划分。在滤波更新阶段,对划分后的量测集进行扩维,从而在形式上将多传感器环境下的跟踪问题转化为单传感器环境下的跟踪问题。高斯逆韦氏序贯PHD滤波算法则先对各个传感器产生的量测集依次进行划分,再依次对每一个划分后的量测集进行滤波,从而达到融合多个传感器量测的目的。仿真结果表明该算法的可行性和有效性。     

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.     

Distributed delay‐dependent filtering for Markovian jump systems interconnected over an undirected graph with time‐delay

In this paper, the problems of delay‐dependent stochastic stability analysis and distributed filter synthesis are considered for Markovian jump systems interconnected over an undirected graph with state time‐invariant delay. A sufficient condition for the well‐posedness, delay‐dependent stochastic stability and contractiveness of the plant is developed in terms of linear matrix inequalities (LMIs). The distributed filter synthesis aims to design a distributed filter inheriting the structure of the plant such that the filtering error systems is well‐posed, delay‐dependent stochastically stable and contractive. Specifically, a corresponding sufficient condition to guarantee the filtering error system contractive is first presented by a set of nonlinear matrix inequalities. Next, for coupling these nonlinear matrix inequalities, a sufficient condition on the existence of such a distributed filter is proposed via a series of finite‐dimensional LMIs. Finally, a numerical simulation is presented to demonstrate the effectiveness of the proposed approach.     

带有无穷分布时滞的不确定系统的鲁棒H_∞滤波器设计

针对一类在系统的状态方程与可测输出中都包含有非线性无穷分布时滞的参数不确定系统,提出一种新颖的鲁棒H∞滤波器的设计方法.设计的鲁棒H∞滤波器可以保证对于带有时变且范数有界的参数不确定性的滤波误差系统是渐近稳定的,并且满足所给定的H∞性能指标.鲁棒H∞滤波器可以通过线性矩阵不等式方法获得.通过一个数值的例子验证了该方法的有效性.     

Observer-enhanced distributed moving horizon state estimation subject to communication delays

In this work, we focus on distributed moving horizon estimation (DMHE) of nonlinear systems subject to time-varying communication delays. In particular, a class of nonlinear systems composed of subsystems interacting with each other via their states is considered. In the proposed design, an observer-enhanced moving horizon state estimator (MHE) is designed for each subsystem. The distributed MHEs exchange information via a shared communication network. To handle communication delays, an open-loop state predictor is designed for each subsystem to provide predictions of unavailable subsystem states (due to delays). Based on the predictions, an auxiliary nonlinear observer is used to generate a reference subsystem state estimate for each subsystem. The reference subsystem state estimate is used to formulate a confidence region for the actual subsystem state. The MHE of a subsystem is only allowed to optimize its subsystem state estimate within the corresponding confidence region. Under the assumption that there is an upper bound on the time-varying delays, the proposed DMHE is proved to give decreasing and ultimately bounded estimation error. The theoretical results are illustrated via the application to a reactor–separator chemical process.     

Adaptive generic model control for a class of nonlinear time-varying processes with input time delay

In this article, an adaptive control approach––Adaptive Generic Model Control (AGMC) for a class of nonlinear time-varying processes with input time delay is proposed. First, a nonlinear state predictor (NSP) is introduced, which extends the conventional generic model control (GMC) to a class of nonlinear processes with input time delay. Then a class of nonlinear time-varying processes with input time delay is further considered. A modified strong tracking filter (MSTF) is adopted to estimate the time-varying parameters of the nonlinear processes, and the state estimates are then utilized to update the plant models used in the NSP and MSTF, this results in an adaptive generic model control scheme for a class of nonlinear time-varying processes with input time delay. A modified mathematical model of a three-tank-system is used for computer simulations, the results show that the proposed AGMC algorithm is satisfactory, and it has definite robustness against model/plant mismatch in the measurement noise.     

Optimal continuous-discrete nonlinear finite memory filter with a discrete predictions

We consider the following problem: using the discrete time measurements of the state variables of a continuous stochastic object and obtain the most precise estimate of these variables. To accelerate the estimating process, we synthesize the optimal structure of a discrete final-dimensional nonlinear filter with a piecewise-constant prediction, remembering the last few measurements in its state vector. The dimension of this vector (i.e., the filter memory volume) can be selected arbitrarily to balance the desired measurement precision and available processing speed for the measurements. We obtain the mean-square optimal structure functions of the filter expressed via the corresponding probability distributions and a chain of Fokker—Planck—Kolmogorov equations to find those distributions. We describe the procedure to obtain the structural functions of the filter numerically by means of the Monte-Carlo method. Also, we provide simple numeric-analytic approximations to the proposed filters: they are compared with approximations to the known filters. An example of the construction of such approximations is considered.     

基于超球面采样UDUTUKF算法的炮弹姿态测定

针对炮弹发射时冲击性高、惯性飞行和飞行时间短的特点,提出一种基于超球面采样的UDUT无迹卡尔曼滤波算法(SS-UDUTUKF),估算炮弹运行姿态。针对炮弹运动规律的特殊性,建立系统的线性状态方程和非线性量测方程,采用三个微机电加速度计作为惯性测量单元测量比力矢量。采用UDUTUKF算法估计非线性系统的状态,抑制滤波的发散,提高滤波的稳定性。SS-UDUTUKF滤波算法具有很好的数值稳定性,能防止估计误差方差阵失去非负定性和对称性,可有效的抑制由于计算舍入误差而造成的滤波不稳定的问题。在三轴飞行测试转台上的仿真实验表明该算法具有很高的估算精度,满足炮弹姿态实时测定的要求。     

Distributed moving horizon state estimation for nonlinear systems with bounded uncertainties

In this work, we propose a distributed moving horizon state estimation (DMHE) design for a class of nonlinear systems with bounded output measurement noise and process disturbances. Specifically, we consider a class of nonlinear systems that are composed of several subsystems and the subsystems interact with each other via their subsystem states. First, a distributed estimation algorithm is designed which specifies the information exchange protocol between the subsystems and the implementation strategy of the DMHE. Subsequently, a local moving horizon estimation (MHE) scheme is designed for each subsystem. In the design of each subsystem MHE, an auxiliary nonlinear deterministic observer that can asymptotically track the corresponding nominal subsystem state when the subsystem interactions are absent is taken advantage of. For each subsystem, the nonlinear deterministic observer together with an error correction term is used to calculate a confidence region for the subsystem state every sampling time. Within the confidence region, the subsystem MHE is allowed to optimize its estimate. The proposed DMHE scheme is proved to give bounded estimation errors. It is also possible to tune the convergence rate of the state estimate given by the DMHE to the actual system state. The performance of the proposed DMHE is illustrated via the application to a reactor-separator process example.     

Critical evaluation of non-linear filter configurations for the state estimation of Continuous Stirred Tank Reactor

A systematic approach has been attempted to design a non-linear observer to estimate the states of a non-linear system. The neural network based state filtering algorithm proposed by A.G. Parlos et al. has been used to estimate the state variables, concentration and temperature in the Continuous Stirred Tank Reactor (CSTR) process. (CSTR) is a typical chemical reactor system with complex nonlinear dynamics characteristics. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration. In this work, the comparison of the performances of an extended Kalman filter (EKF), unscented Kalman filter (UKF) and neural network (NN) based state filter for CSTR that rely solely on concentration estimation of CSTR via measured reactor temperature has been done. The performances of these three filters are analyzed in simulation with Gaussian noise source under various operating conditions and model uncertainties.     

A Bayesian solution and its approximations to out-of-sequence measurement problems

Target tracking using delayed, out-of-sequence measurements is a problem of growing importance due to an increased reliance on networked sensors interconnected via complex communication network architectures. In such systems, it is often the case that measurements are received out-of-time-order at the fusion center. This paper presents a Bayesian solution to this problem and provides approximate, implementable algorithms for both cluttered and non-cluttered scenarios involving single and multiple time-delayed measurements. Such an approach leads to a solution involving the joint probability density of current and past target states.In contrast, existing solutions in the literature modify the sensor measurement equation to account for the time delay and explicitly deal with the resulting correlations that arise in the process noise and current target state. In the Bayesian solution proposed in this paper, such cross correlations are treated implicitly. Under linear Gaussian assumptions, the Bayesian solution reduces to an augmented state Kalman filter (AS-KF) for scenarios devoid of clutter and an augmented state probabilistic data association filter (AS-PDA) for scenarios involving clutter. Computationally efficient versions of AS-KF and AS-PDA are considered in this paper. Simulations are presented to evaluate the performance of these solutions.