Observer design and interval estimation of time-delay discrete-time linear systems with external disturbance and measurement noise

For a class of time-delay discrete-time linear systems with external disturbance and measurement noise, the interval estimation problems of state and measurement noise are investigated in this paper. First, the system state together with the time-delay term and measurement noise is augmented as a new state, and a singular system is then constructed. Subsequently, a kind of decoupling technique is employed to eliminate the effect of external disturbance, and an observer is designed to simultaneously estimate the system state and measurement noise. Based on the estimated state and measurement noise, the interval estimations of system state and measurement noise are obtained by reachability analysis technique. Finally, the effectiveness of the proposed method is verified by a four-tank liquid level system.     

A Feature-based Robust Digital Image Watermarking Against Desynchronization Attacks

In this paper,a new content-based image watermarking scheme is proposed.The Harris-Laplace detector is adopted to extract feature points,which can survive a variety of attacks.The local characteristic regions (LCRs) are adaptively constructed based on scale-space theory.Then,the LCRs are mapped to geometrically invariant space by using image normalization technique.Finally, several copies of the digital watermark are embedded into the nonoverlapped LCRs by quantizing the magnitude vectors of discrete Fourier transform (DFT) coefficients.By binding a watermark with LCR,resilience against desynchronization attacks can be readily obtained.Simulation results show that the proposed scheme is invisible and robust against various attacks which includes common signals processing and desynchronization attacks.     

Active resilient defense control against false data injection attacks in smart grids

The emerging of false data injection attacks (FDIAs) can fool the traditional detection methods by injecting false data, which has brought huge risks to the security of smart grids. For this reason, a resilient active defense control scheme based on interval observer detection is proposed in this paper to protect smart grids. The proposed active defense highlights the integration of detection and defense against FDIAs in smart girds. First, a dynamic physical grid model under FDIAs is modeled, in which model uncertainty and parameter uncertainty are taken into account. Then, an interval observer-based detection method against FDIAs is proposed, where a detection criteria using interval residual is put forward. Corresponding to the detection results, the resilient defense controller is triggered to defense the FDIAs if the system states are affected by FDIAs. Linear matrix inequality (LMI) approach is applied to design the resilient controller with H_{{\infty }} performance. The system with the resilient defense controller can be robust to FDIAs and the gain of the resilient controller has a certain gain margin. Our active resilient defense approach can be built in real time and show accurate and quick respond to the injected FDIAs. The effectiveness of the proposed defense scheme is verified by the simulation results on an IEEE 30-bus grid system.     

Compressed sensing of superimposed chirps with adaptive dictionary refinement

The compressed sensing （CS） theory shows that accurate signal reconstruction depends on presetting an appropriate signal sparsifying dictionary. For CS of superimposed chirps, this dictionary is typically taken to be a waveform-matched dictionary formed by blindly diseretizing the frequency-chirp rate plane. However, since practical target parameters do not lie exactly on gridding points of the assumed dictionary, there is always mismatch between the assumed and the actual sparsifying dictionaries, which will cause the performance of conventional CS reconstruction methods to degrade considerably. To address this, we model the waveform- matched sparsifying dictionary as a parameterized one by treating its sampled frequency-chirp rate grid points as the underlying parameters. As a consequence, the sparsifying dictionary becomes refinable and its refinement can be achieved by optimizing the underlying parameters. Based on this, we develop a novel reconstruction algorithm for CS of superimposed chirps by utilizing the variational expectation-maximization （EM） algorithm. By alternating between steps of sparse coefficients estimation and dictionary parameters optimization, the algorithm integrates the process for dictionary refinement into that of signal reconstruction, and thus can achieve sparse reconstruction and dictionary optimization simultaneously. Experimental results demonstrate that the algorithm effectively deals with the performance degradation incurred by dictionary mismatch, and also outperforms the state-of-the-art CS reconstruction methods both in compressing the signal measurements and in suppressing the measurement noise.     

Ranging-aided relative navigation of multi-platforms

This paper proposes a relative attitude and distance estimation algorithm based on pairwise range measurements between vehicles as well as inertial measurement of each platform. The solution of Wahba''s Problem is introduced to compute the relative attitude between multi-platforms with the sampled pairwise ranges, in which the relative distance estimation is derived and the estimation error distributions are analyzed. An extended Kalman filter is designed to fuse the estimated attitude and distance with the inertial measurement of each platform. The relative poses between platforms are determined without any external aided measurement. To show this novelty, a real testbed is constructed by our research lab. And the experiment results are positive.     

Event-triggered state estimation for T-S fuzzy affine systems based on piecewise Lyapunov-Krasovskii functionals

This paper investigates the problem of event-triggered ${\rm H}_\infty$ state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed ${\rm H}_{\infty}$ performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler''s lemma, the event-triggered ${\rm H}_{\infty}$ observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the event-triggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.     

Robust and fault-tolerant spacecraft attitude control based on an extended-observer design

The aim of this work is to develop a robust control strategy able to drive the attitude of a spacecraft to a reference value, despite the presence of unknown but bounded uncertainties in the system parameters and external disturbances. Thanks to the use of an extended observer design, the proposed control law is robust against all the uncertainties that affect the highfrequency gain matrix, which is shown to capture a broad spectrum of modelling issues, some of which are often neglected by traditional approaches. The proposed controller then provides robustness against parametric uncertainties, as moment of inertia estimation, payload deformations, actuator faults and external disturbances, while maintaining its asymptotic properties.     

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wireless sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.     

Secure quantum report with authentication based on six-particle cluster state and entanglement swapping

A novel high-efficient secure quantum report with authentication based on six-particle cluster state and entanglement swapping is proposed.In our protocol,using N groups of six-particle cluster state,the legitimate users Bob and Charlie send their secret reports to their boss(Alice),who operates sixteen kinds of unitary operations after receiving the reports.Here,entanglement swapping of cluster states and maximum entanglement state measurement are employed by the communicators.It has been proved that our protocol has high level guarantees and honesty,and the scheme is secure not only against the intercept-and-resend attack but also against disturbance attack.     

System identification with binary-valued observations under both denial-of-service attacks and data tampering attacks:defense scheme and its optimality

In this paper,we investigate the defense problem against the joint attacks of denial-of-service attacks and data tampering attacks in the framework of system identification with binary-valued observations.By estimating the key parameters of the joint attack and compensating them in the identification algorithm,a compensation-oriented defense scheme is proposed.Then the identification algorithm of system parameter is designed and is further proved to be consistent.The asymptotic normality of the algorithm is obtained,and on this basis,we propose the optimal defense scheme.Furthermore,the implementation of the optimal defense scheme is discussed.Finally,a simulation example is presented to verify the effectiveness of the main results.     

有适应力的分布式状态估计方法

为提高智能体系统对攻击的免疫力,研究了测量攻击下的适应力分布式状态估计方法。每个智能体对系统状态进行连续的本地线性测量。由于不同智能体的本地测量模型相互异构,对系统状态可能不具有本地可观测性,且攻击者能够操控部分智能体的测量数据,随意改变其测量结果。而智能体的目标是协同处理本地测量数据,并正确估计出未知的系统状态。因此,该问题的挑战在于在不对真实测量数据和恶意智能体的测量数据进行分辨时,如何设计算法估计得到真实的系统状态。为了解决这个问题,设计了适应性分布式最大后验概率估计算法。在该算法中,只要恶意智能体的数量小于某个特定值,所有智能体都能够收敛到系统状态。首先,根据卡尔曼滤波给出集中式最大后验概率(Maximum A Posteriori,MAP)估计方法,并与分布式一致性结合,进而得到分布式最大后验概率估计方法。然后,考虑到测量攻击,从估计一致性的角度,利用自适应饱和度增益设计了适应性分布式最大后验概率估计方法。最后,通过仿真实验验证算法的有效性。     

用于超分辨率重建的同构过完备字典学习方法

构造合适的过完备字典是基于稀疏表示的超分辨率重建中的关键问题之一。在最大似然估计准则下,建立基于混合高斯的同构过完备字典学习模型。模型采用加权的l2范数来刻画分解残差,由分解残差设计权值矩阵,并且将同构的双字典学习问题转化为单字典的学习。采用稀疏编码和字典更新的交替迭代策略完成目标函数的求解,由内点法进行稀疏编码,采用拉格朗日对偶法完成字典更新。最后将学习得到的字典用于超分辨率重建实验,并与其他方法进行比较。实验结果验证了该模型和算法的有效性。     

拒绝服务攻击下基于UKF的智能电网动态状态估计研究

针对连续拒绝服务（Denial of service，DoS）攻击导致量测数据丢失使得动态状态估计失效、进而破坏智能电网安全经济运行问题，本文提出了一种适用拒绝服务攻击的改进无迹卡尔曼滤波（Unscented Kalman filter，UKF）方法，以进行智能电网动态状态估计.首先，分析拒绝服务攻击引起数据丢包特性并设计了数据补偿策略，以重构电力系统动态模型；然后，结合Holt's双参数指数平滑和无迹卡尔曼滤波方法，构造了融合补偿信息的新状态估计方程，并进一步基于估计误差协方差矩阵推导了状态增益更新方法，从而得到了无迹卡尔曼滤波动态状态估计新方法.最后，针对IEEE 30和118节点系统进行仿真，验证了所提方法的可行性和有效性.     

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.      

Security distributed state estimation for nonlinear networked systems against DoS attacks

This paper is concerned with security distributed state estimation for nonlinear networked systems against denial‐of‐service attacks. By taking the effects of resource constraints into consideration, an event‐triggered scheme and a quantization mechanism are employed to alleviate the burden of network. A mathematical model of distributed state estimation is constructed for nonlinear networked systems against denial‐of‐service attacks. Sufficient conditions ensuring the exponential stability of the estimation error systems are obtained by utilizing the Lyapunov stability theory. The explicit expressions of the designed state estimators are acquired in terms of the linear matrix inequalities. Finally, a numerical example is used to testify the feasibility of the proposed method.     

基于协方差矩阵降维稀疏表示的二维波达方向估计

针对稀疏重构下二维波达方向（2D-DOA）估计存在计算量大的问题,提出一种基于协方差矩阵降维稀疏表示的二维波达方向估计方法。首先引入空间角构造流形矢量矩阵冗余字典,将方位角和俯仰角组合从二维空间映射到一维空间,降低了字典的长度和求解复杂度,并且能自动实现俯仰角和方位角配对;其次改进了样本协方差矩阵的稀疏表示模型,对该模型进行了降维处理;然后由协方差矩阵稀疏重构的残差约束特性得到约束残差项置信区间,避免采用正则化方法导致参数选取困难;最后通过凸优化包实现了二维波达方向的估计。仿真实验表明,待选取的协方差矩阵列数达到某个阈值（在只有两个入射信号情况下该值为3）时,可准确实现入射信号角的估计;当信噪比（SNR）较小（<5dB）时,该方法估计精度优于基于空间角的特征矢量算法;低快拍数（<100）下该方法估计精度略低于特征矢量法,但小间隔角度下估计精度与后者相当。     

An Optimal Hybrid Learning Approach for Attack Detection in Linear Networked Control Systems

A novel learning-based attack detection and estimation scheme is proposed for linear networked control systems (NCS), wherein the attacks on the communication network in the feedback loop are expected to increase network induced delays and packet losses, thus changing the physical system dynamics. First, the network traffic flow is modeled as a linear system with uncertain state matrix and an optimal Q-learning based control scheme over finite-horizon is utilized to stabilize the flow. Next, an adaptive observer is proposed to generate the detection residual, which is subsequently used to determine the onset of an attack when it exceeds a predefined threshold, followed by an estimation scheme for the signal injected by the attacker. A stochastic linear system after incorporating network-induced random delays and packet losses is considered as the uncertain physical system dynamics. The attack detection scheme at the physical system uses the magnitude of the state vector to detect attacks both on the sensor and the actuator. The maximum tolerable delay that the physical system can tolerate due to networked induced delays and packet losses is also derived. Simulations have been performed to demonstrate the effectiveness of the proposed schemes.      

Observer‐based attack‐resilient control for linear systems against FDI attacks on communication links from controller to actuators

For the adversarial attacks on the communication links from the controller to the actuators, most of the existing attack‐resilient control results focus on denial‐of‐service attacks. Unlike the existing results, this paper studies the observer‐based attack‐resilient control problem for linear systems with false data injection attacks and process disturbances. Due to limited resources, the malicious attacker is assumed to only manipulate a certain number of communication links from the controller to the actuators. A novel control architecture is proposed, which consists of an attack‐resilient state observer, a controller gain scheme, and a supervisory switching strategy. The observer is developed based on the maximin strategy, and state estimation will be used to construct the controller. The switching strategy is designed to pick an appropriate controller gain and prevent the attack signals from entering the plant automatically. It is shown that the closed‐loop system is stable with an attack‐resilient performance. Finally, to verify the effectiveness of the proposed controller, simulation results on a linearized reduced‐order aircraft system and an IEEE six‐bus power system are provided.     

Resilient Time-Varying Formation-Tracking of Multi-UAV Systems Against Composite Attacks: A Two-Layered Framework

This paper studies the countermeasure design problems of distributed resilient time-varying formation-tracking control for multi-UAV systems with single-way communications against composite attacks, including denial-of-services (DoS) attacks, false-data injection attacks, camouflage attacks, and actuation attacks (AAs). Inspired by the concept of digital twin, a new two-layered protocol equipped with a safe and private twin layer (TL) is proposed, which decouples the above problems into the defense scheme against DoS attacks on the TL and the defense scheme against AAs on the cyber-physical layer. First, a topology-repairing strategy against frequency-constrained DoS attacks is implemented via a Zeno-free event-triggered estimation scheme, which saves communication resources considerably. The upper bound of the reaction time needed to launch the repaired topology after the occurrence of DoS attacks is calculated. Second, a decentralized adaptive and chattering-relief controller against potentially unbounded AAs is designed. Moreover, this novel adaptive controller can achieve uniformly ultimately bounded convergence, whose error bound can be given explicitly. The practicability and validity of this new two-layered protocol are shown via a simulation example and a UAV swarm experiment equipped with both Ultra-WideBand and WiFi communication channels.      

恶意攻击下基于分布式稀疏优化的安全状态估计

恶意生成的量测攻击信号是导致信息物理系统(Cyber-physical system, CPS)探测失效的主要原因, 如何有效削弱其影响是实现精准探测、跟踪与感知的关键问题. 分布式传感器网络(Distributed sensor network, DSN)依靠多传感器协作与并行处理突破单一监测节点的任务包线, 能够显著提升探测系统跟踪精度与可靠性. 首先, 依据压缩感知理论, 将单一节点的目标运动状态估计建模为一种基于l₀范数最小化的稀疏优化问题, 采用正交匹配追踪法(Orthogonal matching pursuit, OMP)重构量测攻击信号, 以克服采用凸优化算法求解易陷入局部最优的缺陷. 通过卡尔曼滤波量测更新抵消攻击信号影响, 恢复目标运动的真实状态. 其次, 针对错误注入攻击等复杂量测攻击形式, 基于势博弈理论, 提出一种分布式稀疏优化安全状态估计方法, 利用多传感器节点信息交互与协作提升探测与跟踪的稳定性. 仿真结果表明, 所提方法在分布式传感器网络协作抵抗恶意攻击方面具有优越性.