拒绝服务攻击下领导-跟随多智能体系统的均方一致性研究

针对带有过程噪声和测量噪声的领导-跟随多智能体系统,研究拒绝服务攻击下多智能体系统的一致性问题.首先,设计基于卡尔曼滤波的状态观测器,对智能体状态进行有效准确的估计;然后,基于预测控制理论提出一种基于状态估计信息的分布式预测控制算法,从而实现领导-跟随多智能体系统的均方一致性控制,并给出拒绝服务攻击环境下实现领导-跟随多智能体系统均方一致性的充分必要条件;最后,通过数值仿真验证所提出方法的正确性和有效性.     

基于自适应Kalman滤波的智能电网假数据注入攻击检测EI北大核心CSCD

研究了一种针对智能电网中假数据注入攻击的有效检测方法.假数据注入攻击可以保持攻击前后残差基本不变,绕过传统的不良数据检测技术.首先基于电网模型,分析了假数据注入攻击的攻击特性,针对噪声统计特性未知且无迹Kalman滤波(Unscented Kalman filter,UKF)不稳定的现象,提出了自适应平方根无迹Kalman滤波改进算法.基于状态估计值,结合中心极限定理提出检测算法,并与欧几里得检测方法、巴氏系数检测方法进行比较.最后,仿真表明本文所提检测算法的优越性.     

分布式电网CPS 系统数据攻击下的状态估计

电力物理网络通过构建信息网络进行优化调控并构成信息物理融合系统, 实现大规模分布式系统的优化控制, 随之而来的问题是病毒、黑客入侵、拒绝服务等来自信息网络的威胁, 导致物理系统恶意破坏. 鉴于此, 以攻击可检测为前提, 建立攻击信号下的电力系统分布式动态模型, 设计动态状态估计器检测受攻击的信号, 并估计其原始信号. 最后通过3 机9 节点分布式电网系统仿真实验验证了所设计的状态估计器对于数据攻击检测的有效性.

     

基于自适应无迹卡尔曼滤波QAR数据降噪研究

针对QAR数据包含离群值、噪声值等异常数据严重影响数据分析的问题,提出了一种自适应无迹卡尔曼滤波的数据降噪方法.利用改进拉依达准则剔除粗大误差数据,以无迹卡尔曼滤波为基础,结合Sage-Husa噪声估计器对系统噪声进行实时预测和修正,有效地解决了系统噪声时变的问题.利用空客A330飞机的数据样本对算法有效性进行了数值验证,仿真结果表明,自适应无迹卡尔曼滤波算法估计精度更高,降噪效果更优.研究可提高基于QAR数据分析与挖掘工作的数据质量.     

动力锂电池SOC估计仿真研究

针对传统的无迹卡尔曼滤波算法(UKF)估计动力锂电池荷电状态(SOC)时,由于滤波迭代过程中系统噪声不确定,可能导致估计结果精度欠佳的问题,提出一种改进的自适应无迹卡尔曼滤波算法(AUKF)动态地估计锂离子电池的SOC.算法以UKF算法为基础,引入改进的Sage-Husa自适应滤波算法,利用观测数据进行滤波递推的同时,实时更新系统噪声的统计特性.以等效电路模型为基础,采用递推最小二乘法辨识模型参数,应用AUKF算法对电池SOC进行估算,并从实际工况进行仿真验证分析.仿真结果表明,上述算法有效的提高了估计精度,误差稳定性较高.     

基于拉依达准则与线性拟合的改进型无迹卡尔曼滤波粗大误差补偿算法

无迹卡尔曼滤波是卡尔曼滤波技术的重要组成部分,它有效地克服了扩展卡尔曼滤波的估计精度低、稳定性差等缺陷。然而无迹卡尔曼滤波未考虑粗大误差(如离群值、静差和漂移)的影响。目标跟踪经常受到不同种类粗大误差的影响,研究无迹卡尔曼滤波器对粗大误差的检测和补偿,对目标跟踪准确性的提高有重大意义。本文针对观测值中各种粗大误差影响目标跟踪精度的问题,采用拉依达准则对观测值进行检测。为了对误差进行补偿,本文提出了一种观测数据残差线性拟合的方法,使用拟合产生的预测残差补偿粗大误差,使补偿后的目标运动轨迹能够减小粗大误差的干扰。经过目标跟踪仿真实验和对比,本文提出的改进型无迹卡尔曼滤波算法能有效地减小粗大误差观测值对状态预测过程的影响,能实现对目标的准确跟踪,提高了滤波的稳定性和准确性。     

带非线性等式约束无迹卡尔曼滤波方法

针对带非线性等式约束的非线性系统的状态估计问题,给出了一种新形式的基于无迹卡尔曼滤波及伪观测手段的处理约束的状态估计方法（SPUKF）。在该方法中原动态系统被虚拟地分离成两个并行的子系统,各时刻的状态估计由基于这两个子系统构建的两套滤波链交替得到。相对于伪观测法中的序贯形式估计器,SPUKF无需事先确定观测及约束的处理次序且能获得更好的估计结果,故可以用来解决序贯方法中观测与约束的处理次序问题。由钟摆运动的实例仿真结果看到,SPUKF不仅有好于序贯形式无迹卡尔曼滤波的估计效果,误差改善比达到22%左右,而且算法运行时间与序贯形式估计器相近。此外,其估计效果还与批处理无迹卡尔曼滤波相当。     

新息序列驱动的无人机控制系统数据攻击检测

伴随物联网和自主系统的不断发展,信息物理系统的网络安全备受关注.无人机是一种典型的依靠通信和控制系统实现自主飞行的智能装置,其安全性尤为突出.本文针对无人机的状态估计算法,考虑其传感器和控制指令受到数据攻击,提出基于扩展卡尔曼滤波的新息序列状态估计检测方法.首先建立无人机信息物理模型,引入状态估计算法和数据攻击模型.然后,利用新息序列构造标量检测统计量用于数据攻击检测,并针对飞行器机动造成的状态跳变引入负无穷范数,用以降低数据攻击检测的误检率.最后,通过仿真实验验证所提出的检测方法能有效检测不同威胁模式下和状态下无人控制系统的数据攻击.     

室内环境下同步定位与地图创建改进算法

提出了一种室内环境下基于平方根无迹卡尔曼滤波（SRUKF）的同步定位与地图创建（SLAM）算法． 该方法在每步迭代中采用平方根无迹粒子滤波器进行机器人状态估计,并引入平方根无迹卡尔曼滤波器定位路标, 进而完成机器人状态和相应路标信息更新．将本文算法与机器人运动模型和红外标签观测模型结合进行了仿真和实 验,结果表明,本算法在同步定位和地图创建过程中提高了机器人状态和路标估计的精度及稳定性．     

基于SIR模型的智能电网WCSN数据伪造攻击研究

为了解决智能电网无线传感器网络面临的异构无线网络共存、频谱资源紧张和海量数据处理等问题,在智能电网引入了认知无线传感器网络（WCSN）。文章对智能电网WCSN中的数据伪造攻击进行了研究。在此类攻击中,恶意认知无线传感器节点（传染节点）通过向其他认知无线传感器节点（易感节点）发送伪造的频谱感知数据和设备能耗信息,导致控制中心做出错误的频谱分配和电力调度决策。采用流行病理论中的SIR模型,对智能电网WCSN中的数据伪造攻击信息传播过程进行了建模,研究了流行病爆发的潜在决定因素。最后,通过仿真验证了智能电网WCSN数据伪造攻击SIR模型,并对系统动态特性进行了分析。     

Maximum correntropy unscented filter

The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilising a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimator leads to the well-known unscented Kalman filter (UKF). Although the UKF works very well in Gaussian noises, its performance may deteriorate significantly when the noises are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises. To improve the robustness of the UKF against impulsive noises, a new filter for non-linear systems is proposed in this work, namely the maximum correntropy unscented filter (MCUF). In MCUF, the UT is applied to obtain the prior estimates of the state and covariance matrix, and a robust statistical linearisation regression based on the maximum correntropy criterion is then used to obtain the posterior estimates of the state and covariance matrix. The satisfying performance of the new algorithm is confirmed by two illustrative examples.     

A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

This paper explores multiple model adaptive estimation (MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter-multiple model adaptive estimation unscented Kalman filter (MMAE-UKF) rather than conventional Kalman filter methods, like the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters, which the improved filtering method can overcome. Meanwhile, this algorithm is used for integrated navigation system of strapdown inertial navigation system (SINS) and celestial navigation system (CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.      

Power system observability and dynamic state estimation for stability monitoring using synchrophasor measurements

Growing penetration of intermittent resources such as renewable generations increases the risk of instability in a power grid. This paper introduces the concept of observability and its computational algorithms for a power grid monitored by the wide-area measurement system (WAMS) based on synchrophasors, e.g. phasor measurement units (PMUs). The goal is to estimate real-time states of generators, especially for potentially unstable trajectories, the information that is critical for the detection of rotor angle instability of the grid. The paper studies the number and siting of synchrophasors in a power grid so that the state of the system can be accurately estimated in the presence of instability. An unscented Kalman filter (UKF) is adopted as a tool to estimate the dynamic states that are not directly measured by synchrophasors. The theory and its computational algorithms are illustrated in detail by using a 9-bus 3-generator power system model and then tested on a 140-bus 48-generator Northeast Power Coordinating Council power grid model. Case studies on those two systems demonstrate the performance of the proposed approach using a limited number of synchrophasors for dynamic state estimation for stability assessment and its robustness against moderate inaccuracies in model parameters.     

An adaptive unscented Kalman filter approach to secure state estimation for wireless sensor networks

Wireless sensor networks are vulnerable to false data injection attacks, which may mislead the state estimation. To solve this problem, this paper presents a chi-square test-based adaptive secure state estimation (CTASSE) algorithm for state estimation and attack detection. Taking advantage of Kalman filters, attack signal together with process noise or measurement noise are described as total white Gaussian noise with uncertain covariance matrix. The chi-square test method is used in the adaptation of the total noise covariance and attack detection. Then, a standard adaptive unscented Kalman filter (UKF) is used for the state estimation. Finally, simulation results show that the proposed CTASSE algorithm performs better than other UKFs in state estimation and is also effective in real-time attack detection.     

Unscented卡尔曼滤波在状态估计中的应用

针对非线形系统的滤波问题，无法使用卡尔曼滤波器（KF），扩展卡尔曼滤波（EKF）方法虽能应用于非线形系统，但给出的是状态的有偏估计，并且对模型误差的鲁棒性较差。为了给出更好的状态估计值，该文介绍了Unscented卡尔曼滤波（UKF）的基本原理。其思想是：基于unscented变换，UKF滤波算法能够给出更精确的均值和协方差的估计，从而带来更高的精度。最后通过Mackey—Glass模型时间序列的状态估计仿真实侧说明：同EKF相比，UKF的滤波精度和稳定性都显著提高了，还可避免计算烦琐的Jacobi矩阵，是一种良好的非线性滤波方法。     

双层无迹卡尔曼滤波

针对无迹卡尔曼滤波（Unscented Kalman fllter,UKF）在强非线性系统中估计效果差的问题,提出了双层无迹卡尔曼滤波（Double layer unscented Kalman filter,DLUKF）算法,该算法用带权值的采样点表征先验分布,而后用内层UKF算法对每个采样点进行更新,最后引入外层UKF算法的更新机制得到估计值和估计协方差.仿真结果表明,相比于传统算法,所提的DLUKF算法可以在较低计算负载下获得较高滤波估计精度.     

Interacting multiple model estimation-based adaptive robust unscented Kalman filter

The unscented Kalman filter (UKF) is a promising approach for the state estimation of nonlinear dynamic systems due to its simple calculation process and superior performance in highly nonlinear systems. However, its solution will be degraded or even divergent when the system model involves uncertainty. This paper presents an interacting multiple model (IMM) estimation-based adaptive robust UKF to address this problem. This method combines the merits of the adaptive fading UKF and robust UKF and discards their demerits to inhibit the disturbance of system model uncertainty on the filtering solution. An adaptive fading UKF for the case of process model uncertainty and a robust UKF for the case of measurement model uncertainty are established based on the principle of innovation orthogonality. Subsequently, an IMM estimation is developed to fuse the adaptive fading UKF and robust UKF as sub-filters according to the mode probability. The system state estimation is achieved as a probabilistic weighted sum of the estimation results from the two sub-filters. Simulations, experiments and comparison analysis validate the efficacy of the proposed method.     

State of charge estimation for Li-ion battery based on model from extreme learning machine

Lithium-ion (Li-ion) battery state of charge (SOC) estimation is important for electric vehicles (EVs). The model-based state estimation method using the Kalman filter (KF) variants is studied and improved in this paper. To establish an accurate discrete model for Li-ion battery, the extreme learning machine (ELM) algorithm is proposed to train the model using experimental data. The estimation of SOC is then compared using four algorithms: extended Kalman filter (EKF), unscented Kalman filter (UKF), adaptive extended Kalman filter (AEKF) and adaptive unscented Kalman filter (AUKF). The comparison of the experimental results shows that AEKF and AUKF have better convergence rate, and AUKF has the best accuracy. The comparison from the radial basis function neural network (RBF NN) model also verifies that the ELM model has lighter computation load and smaller estimation error in SOC estimation process. In general, the performance of Li-ion battery SOC estimation is improved by the AUKF algorithm applied on the ELM model.     

基于UKF 和神经网络的一类非线性系统状态估计

在模型未知的情况下,估计过程的重要变量尤为重要.鉴于此,采用不敏卡尔曼滤波(UKF)与神经网络相结合的方法,解决一类未知模型非线性系统的状态估计问题.采用动态神经网络对非线性系统进行建模,利用UKF对状态和权值进行同时更新,从而达到神经网络逼近真实模型,估计值跟随真实值的目的.通过两个仿真实例表明了所提出的方法具有良好的估计效果,并且状态在输出中的比重越大,其估计精度越高.