Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks,utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism(DETM).In order to optimize communication resource utilization,the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not.To guarantee a satisfactory estimation performance for the fault signal,an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals.The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains.The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator,ensuring that the specified performance requirements are met under certain conditions.Then,the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided.Finally,a numerical example is conducted to demonstrate the effectiveness of the designed estimator.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

Particle filter for joint frequency offset and channel estimation in MIMO-OFDM systems

A particle filter is proposed to perform joint estimation of the carrier frequency offset （CFO） and the channel in multiple-input multiple-output orthogonal frequency division multiplexing （MIMO-OFDM） wireless communication systems. It marginalizes out the channel parameters from the sampling space in sequential importance sampling （SIS）, and propagates them with the Kalman filter. Then the importance weights of the CFO particles are evaluated according to the imaginary part of the error between measurement and estimation. The varieties of particles are maintained by sequential importance resampling （SIR）. Simulation results demonstrate this algorithm can estimate the CFO and the channel parameters with high accuracy. At the same time, some robustness is kept when the channel model has small variations.

Evaluation of Preamble Based Channel Estimation for MIMO-FBMC Systems

Filter-bank multicarrier （FBMC） with offset quadrature amplitude modulation （OQAM） is a candidate waveform for future wireless communications due to its advantages over orthogonal frequency division multiplexing （OFDM） systems. However, because of or-thogonality in real field and the presence of imaginary intrinsic interference, channel estimation in FBMC is not as straightforward as OFDM systems especially in multiple antenna scenarios. In this paper, we propose a channel estimation method which employs intrinsic interference cancellation at the transmitter side. The simulation results show that this method has less pilot overhead, less peak to average power ratio （PAPR）, better bit error rate （BER）, and better mean square error （MSE） performance compared to the well-known intrinsic approximation methods （IAM）.

Dynamic State Estimation for DFIG with Unknown Inputs Based on Cubature Kalman Filter and Adaptive Interpolation

Dynamic state estimation(DSE)accurately tracks the dynamics of power systems and demonstrates the evolution of the system state in real time.This paper proposes a DSE approach for a doubly-fed induction generator(DFIG)with unknown inputs based on adaptive interpolation and cubature Kalman filter(AICKF-UI).DFIGs adopt different control strategies in normal and fault conditions;thus,the existing DSE approaches based on the conventional control model of DFIG are not applicable in all cases.Consequently,the DSE model of DFIGs is reformulated to consider the converter controller outputs as unknown inputs,which are estimated together with the DFIG dynamic states by an exponential smoothing model and augmented-state cubature Kalman filter.Furthermore,as the reporting rate of existing synchro-phasor data is not sufficiently high to capture the fast dynamics of DFIGs,a large estimation error may occur or the DSE approach may diverge.To this end,in this paper,a local-truncation-error-guided adaptive interpolation approach is developed.Extensive simulations conducted on a wind farm and the modified IEEE 39-bus test system show that the proposed AICKF-UI can(1)effectively address the divergence issues of existing cubature Kalman filters while being computationally more efficient;(2)accurately track the dynamic states and unknown inputs of the DFIG;and(3)deal with various types of system operating conditions such as time-varying wind and different system faults.

含未知输入非线性系统的扩展平方根容积卡尔曼滤波算法

针对工程实际应用中存在的未知输入会导致经典的非线性滤波器状态估计精度下降甚至滤波发散的问题,提出了一种基于最小方差无偏估计(minimum variance unbiased estimation,MVUE)准则的扩展平方根容积卡尔曼滤波(extended square-root cubature Kalman filter,ESRCKF)算法。首先,结合上一时刻未知输入估计值对状态一步预测值进行修正,得到含未知输入条件下的状态预测值。其次,设计新息并采用加权最小二乘(weighted least squares,WLS)法获取当前时刻未知输入的无偏估计。最后,通过最小化协方差矩阵的迹,同时采用拉格朗日乘子法和舒尔补引理得到系统状态的最小方差无偏估计。仿真结果表明,相比于现有的非线性滤波算法,ESRCKF算法提高了在处理含未知输入非线性系统时的状态估计精度,并能同时实现系统状态和未知输入的最优估计,验证了该算法的有效性。

Robust extended Kalman filter with input estimation for maneuver tracking

This study investigates the problem of tracking a satellite performing unknown continuous maneuvers. A new method is proposed for estimating both the state and maneuver acceleration of the satellite. The estimation of the maneuver acceleration is obtained by the combination of an unbiased minimum-variance input and state estimation method and a low-pass filter. Then a threshold-based maneuver detection approach is developed to determinate the start and end time of the unknown maneuvers. During the maneuvering period, the estimation error of the maneuver acceleration is modeled as the sum of a fluctuation error and a sudden change error. A robust extended Kalman filter is developed for dealing with the acceleration estimate error and providing state estimation. Simulation results show that, compared with the Unbiased Minimum-variance Input and State Estimation(UMISE) method, the proposed method has the same position estimation accuracy, and the velocity estimation error is reduced by about 5 times during the maneuver period. Besides, the acceleration detection and estimation accuracy of the proposed method is much higher than that of the UMISE method.

基于结构随机场的桥梁移动荷载统计矩识别

由于结构、材料不确定等因素,桥梁结构往往具有随机性,因此,基于桥梁结构的响应识别得到的桥梁所受移动荷载也是不确定的,但相关研究还较少。为此,本文研究桥梁结构为空间随机场的情况下,有效识别移动荷载的统计矩。提出的识别方法基于桥梁结构随机场的Karhunen⁃Loeve(KL)展开、未知输入的卡尔曼滤波与改进的两点估计法的结合。桥梁结构参数为空间相关的随机场,通过KL展开将随机场转化为多随机变量的组合。在多随机变量下的不确定传播中,利用改进的两点估计法,将识别移动荷载统计矩问题转化为若干个确定性识别移动荷载识别的逆问题。采用未知输入的卡尔曼滤波进行确定性识别移动荷载,可有效估计识别移动荷载的统计矩。通过数值模拟算例验证了提出的估计方法的有效性。

基于UIO/双KF的4WID-EV驱动轮纵向力估计

四轮独立驱动电动汽车(4WID-EV)能独立实现4个轮胎纵向力的控制与分配,实时保证电动汽车的最佳运行状态。基于单个驱动轮的动力学和轮毂电机特性建立了驱动轮模型,采用电流、电压、转速等常用参数设计轮胎纵向力估计模型。考虑系统结构参数的不确定性和传感器噪声干扰,将驱动轮纵向力估计与干扰分离,基于卡尔曼滤波算法,设计了驱动轮纵向力-未知输入观测器。仿真和台架道路模拟试验结果表明,设计的观测系统能实时估计驱动轮纵向力,且具有较高的估计精度和抗干扰性,能满足四轮独立驱动电动汽车动力学控制要求。

面向后三代移动通信的MIMO-GMC无线传输技术

面向后三代移动通信应用 ,本文提出多入多出 (MIMO)信道环境下广义多载波 (GMC)无线传输技术方案 ,对其中若干关键技术进行了简要阐述 ,包括广义多载波合成与分析系统的设计与实现、双循环自适应时隙结构、导频设计与信道估计、迭代检测译码方法等 ,并给出瑞利衰落MIMO信道环境下链路仿真结果 .

基于新息序列修正的输入估计算法

针对传统输入估计算法跟踪机动目标时检测概率和估计精度不高的缺点,提出一种改进的输入估计跟踪算法。算法利用对检测窗内新息序列进行修正以及对卡尔曼滤波状态变量进行补偿的方法,消除了已有机动检测估计对后继机动输入估计的影响,提高了对机动的检测概率和估计精度。理论分析和仿真结果表明,改进后的算法相比原有算法和IMM3算法,不仅跟踪性能有显著提高,而且对加速度缓变的机动也有较好的跟踪能力。

考虑路面不平输入的车辆侧倾状态估计及主动控制

为了低成本且高精度地估计车辆侧倾状态,研究考虑路面不平输入的车辆侧倾状态估计及主动防侧倾控制方法。建立三自由度侧向动力学模型,利用Kalman滤波算法设计基于侧向动力学的车辆侧倾状态估计算法。为了估计由路面不平引起的侧倾状态,建立七自由度垂向动力学模型,利用可测的车身垂向加速度信号设计基于垂向动力学的车辆侧倾状态估计算法。在此基础上,利用滑模变结构控制方法来决策车辆所期望的附加防侧倾力矩,通过阻尼可调减振器的阻尼力实现车辆主动防侧倾控制。仿真结果表明,基于侧向动力学的侧倾状态估计算法只对转向输入引起的侧倾具有较好地估计效果,而基于垂向动力学的估计算法能够有效地估计由转向输入和路面不平输入引起的侧倾状态,并且利用阻尼可调减振器能够有效地实现车辆侧倾状态的主动控制。

一种面向临近空间高超声速再入滑翔目标跟踪算法

针对临近空间高超声速再入滑翔目标的跟踪问题,提出了一种基于回顾成本输入估计的无偏转换量测卡尔曼滤波(Retrospective cost input estimation-unbiased converted measurements Kalman filter, RCIE-UCMKF)。首先,根据再入滑翔目标的飞行特性,将加速度看成是未知的确定输入构建运动学跟踪模型;然后,对目标的非线性量测信息进行无偏转换,并将得到的噪声协方差矩阵进行解耦,降低算法的复杂度;最后,利用回顾成本的输入估计对未知加速度进行重构,采用递推最小二乘法更新输入估计器的参数矩阵,同时将估计的加速度引入到卡尔曼滤波框架下,实现对高超声速再入滑翔目标状态的准确估计。仿真结果表明了该算法的有效性和可行性。

TBM末端机动弹道预测

TBM末端机动弹道的预测是现代防空C3I系统建模中重要的组成部分,它直接影响着反导武器系统对TBM拦截的精度与概率。为了精确地预测出处于末端机动条件的TBM弹道,并满足防空作战实时性的要求,本文在广义卡尔曼滤波的基础上提出了一种基于输入估计的自适应滤波方法。结论表明,该方法较其它方法更适应于解决TBM末端机动弹道预测问题,具有很好的军事应用价值。

基于Kalman滤波的通用和统一的白噪声估计方法

用射影理论,基于Kalman滤波提出了通用和统一的白噪声估计方法,可统一解决带非零均值相关噪声的线性离散时变随机控制系统的白噪声滤波、平滑和预报问题.提出了输入白噪声估值器和观测白噪声估值器,最优和稳态白噪声估值器,固定点、固定滞后和固定区间白噪声平滑器,白噪声新息滤波器和Wiener滤波器.它可应用于石油地震勘探信号处理和状态估计,为解决信号和状态估计问题,提供了新的途径和工具.关于Bernoulli-Gaussian白噪声估值器的仿真例子说明了其有效性.

一种改进的机动目标跟踪方法

机动目标跟踪方法主要有变维滤波、输入估计方法和交互式多模方法。由于输入估计方法采用常速模型作为状态方程对目标进行滤波跟踪,避免了模型集设计,所以成为研究的热点。但传统输入估计方法跟踪机动目标时存在机动检测时延较长和估计精度不高的缺点。针对以上问题,本文对输入估计算法进行了改进。用变检测窗长度的方法提高机动检测的响应速度,通过对检测窗内新息序列的修正显著提高了估计精度。仿真结果验证改进后的算法比原有算法的跟踪性能有显著提高,且对加速度随时间变化的机动也有一定的跟踪能力。

一种多级助推段弹道导弹跟踪算法

针对多级助推段弹道导弹存在多个加速度突变点、机动性强导致跟踪难度大的问题,提出一种基于量测转换的强跟踪输入估计(STMIE)弹道导弹跟踪算法。在对多级助推段弹道导弹动力学特性分析的基础上,推导出雷达站东北上(ENU)坐标系下弹道导弹的运动方程,并通过将修正的无偏量测转换(MUCM)与STMIE算法融合,使得在导弹跟踪中应用STMIE算法成为可能。以典型的多级助推段弹道导弹为跟踪目标进行仿真校验,结果表明,本文算法对多级助推段弹道导弹目标跟踪性能优越,且算法复杂度较低,效费比高。

基于Jerk输入估计的MCS模型及非线性跟踪算法

针对强机动目标跟踪问题,基于当前统计(CS,Current Statistical)模型、改进输入估计(MIE,Modified Input Estimation)和无迹强跟踪滤波器,提出了一种新的自适应目标跟踪算法.该算法引入Jerk输入估计改进了当前统计模型的状态方程和机动加速度方差调整方法,利用改进的无迹强跟踪滤波器实现了状态协方差、状态噪声协方差和机动频率的联合自适应.在没有加速度先验知识的情况下,能够实时准确跟踪目标连续强机动、匀加速机动和匀速运动状态.仿真实验表明:相比CS模型无迹滤波算法、CS模型无迹强跟踪算法和交互多模型算法,该算法在对目标强机动的适应性、跟踪精度和对突变状态跟踪的收敛性方面都有更好的性能.

基于数值微分技术构造鲁棒估计模型的方法及其应用仿真

在研究宏观空间运动特性的基础上,利用数值微分作为表达工具描述未知运动过程的动态特性,构造了数值微分型滤波模型(NDFM)和数值微分型滤波-预报联合模型(NDFPM)。这种方法能根据各种应用要求对动态特性完全未知的运动过程建立结构简单、鲁棒性强的估计模型,而且容易选择估计算法获得满意的性能。本文对未知扰动作用下的被控过程建立NDFM并实现状态重构和扰动补偿;对动态未知的被跟踪目标建立NDFPM并估计出运动参数的当前值和一步预报值。仿真结果表明这两种模型具有较强的鲁棒性和满意的估计精度。

盲分离模型用于相关噪声的滤波问题

在实际应用中经常需要从观测数据中提取出期望的信号 .现有的基于滤波器模型的算法中估计滤波器系数的Weiner_Hopf方程有唯一解要求信号的相关矩阵是非奇异的 ,并且要得到精确的估计结果需要的滤波器阶数很高导致计算量很大 ,不便于实时处理 .首次将盲分离模型用于线性估计问题的滤波问题 ,算法克服了上述基于滤波器模型的局限性 ,能对信号进行有效的提取 .