Multi-channel SAR imaging based on distributed compressive sensing

The rapid development of compressive sensing(CS)shows that it is possible to recover a sparse signal from very limited measurements.Synthetic aperture radar(SAR)imaging based on CS can reconstruct the target scene with a reduced number of collected samples by solving an optimization problem.For multi-channel SAR imaging based on CS,each channel requires sufficient samples for separate imaging and the total number of samples could still be large.We propose an imaging algorithm based on distributed compressive sensing(DCS)that reconstructs scenes jointly under multiple channels.Multi-channel SAR imaging based on DCS not only exploits the sparsity of the target scene,but also exploits the correlation among channels.It requires significantly fewer samples than multi-channel SAR imaging based on CS.If multiple channels offer different sampling rates,DCS joint processing can reconstruct target scenes with a much more flexible allocation of the number of measurements offered by each channel than that used in separate CS processing.     

A novel spaceborne SAR wide-swath imaging approach based on Poisson disk-like nonuniform sampling and compressive sensing

Since the range swath width in the conventional single channel spaceborne synthetic aperture radar(SAR)is restricted by the system parameters,there is a trade-off between the azimuth resolution and the swath width in order to satisfy the Nyquist sampling criterion.In this paper,we propose a novel spaceborne SAR wide-swath imaging scheme based on compressive sensing(CS)for the sparse scene.The proposed method designs a Poisson disk-like nonuniform sampling pattern in the azimuth direction,which meets the demand of wider swath by restricting the smallest time interval between any two azimuth samples,with the conventional sampling pattern preserved in the range direction.By a similar way to the processing procedure of spectral analysis(SPECAN)algorithm,the linear range migration correction(RMC)is realized while carrying out range compression,which can meet the demand for focusing with middle level resolution.To reduce the computation load of CS reconstruction,we propose a novel fast reconstruction algorithm based on nonuniform fast Fourier transform(NUFFT),which greatly reduces the computation complexity from O(2M N)to O(4N log N).Experiment results validate the effectiveness of the proposed methods via the point target simulation and the Radarsat-1 raw data processing in F2 mode.     

An evaluation framework for energy aware buildings using statistical model checking

Cyber-physical systems are to be found in numerous applications throughout society.The principal barrier to develop trustworthy cyber-physical systems is the lack of expressive modelling and specification formalisms supported by efficient tools and methodologies.To overcome this barrier,we extend in this paper the modelling formalism of the tool UPPAAL-SMC to stochastic hybrid automata,thus providing the expressive power required for modelling complex cyber-physical systems.The application of Statistical Model Checking provides a highly scalable technique for analyzing performance properties of this formalisms.A particular kind of cyber-physical systems are Smart Grids which together with Intelligent,Energy Aware Buildings will play a major role in achieving an energy efficient society of the future.In this paper we present a framework in UPPAAL-SMC for energy aware buildings allowing to evaluate the performance of proposed control strategies in terms of their induced comfort and energy profiles under varying environmental settings(e.g.weather,user behavior etc.).To demonstrate the intended use and usefulness of our framework,we present an application to the Hybrid Systems Verification Benchmark.     

Waveform design and high-resolution imaging of cognitive radar based on compressive sensing

We introduce the compressive sensing(CS) theory for waveform design of cognitive radar,and then propose an algorithm for the high-resolution radar signal waveform and its corresponding imaging method based on the sparse orthogonal frequency division multiplexing-linear frequency modulation(OFDM-LFM) signal.We first present the principle of spectrum synthesis and high-resolution imaging based on OFDM-LFM signals.Then,we propose the spectrum-sparse waveform design criterion and the reconstruction algorithm for a highresolution range profile(HRRP) based on CS.Based on this,we analyze in detail the relationship between the scattering characteristics of the target and the parameters of the designed signal,and we construct the feedback of the target characteristics on the waveforms.Therefore,the "cognitive" function of radar can be achieved by adaptively adjusting the waveform with the target characteristics.Simulations are given to validate the effectiveness of the proposed algorithm.     

Spotlight SAR sparse sampling and imaging method based on compressive sensing

Spotlight synthetic aperture radar(SAR)emits a chirp signal and the echo bandwidth can be reduced through dechirp processing,where the A/D sampling rate decreases accordingly at the receiver.Compressive sensing allows the compressible signal to be reconstructed with a high probability using only a few samples by solving a linear program problem.This paper presents a novel signal sampling and imaging method for application to spotlight SAR based on compressive sensing.The signal is randomly sampled after dechirp processing to form a low-dimensional sample set,and the dechirp basis is imported to reconstruct the dechirp signal.Matching pursuit(MP)is used as a reconstruction algorithm.The reconstructed signal uses polar format algorithm(PFA)for imaging.Although our novel mechanism increases the system complexity to an extent,the data storage requirements can be compressed considerably.Several simulations verify the feasibility and accuracy of spotlight SAR signal processing via compressive sensing,and the method still obtains acceptable imaging results with 10%of the original echo data.     

The essential ability of sparse reconstruction of different compressive sensing strategies

We show the essential ability of sparse signal reconstruction of different compressive sensing strategies,which include the L1 regularization,the L0 regularization(thresholding iteration algorithm and OMP algorithm),the Lq(0 < q ≤ 1) regularizations,the Log regularization and the SCAD regularization.Taking phase diagram as the basic tool for analysis,we find that(i) the solutions of the L0 regularization using hard thresholding algorithm and OMP algorithm are similar to those of the L1 regularization;(ii) the Lq regularization with the decreasing value of q,the Log regularization and the SCAD regularization can attain sparser solutions than the L1 regularization;(iii) the L1/2 regularization can be taken as a representative of the Lq(0 < q < 1) regularizations.When 1/2 < q < 1,the L1/2 regularization always yields the sparsest solutions and when 0 < q < 1/2 the performance of the regularizations takes no significant difference.The results of this paper provide experimental evidence for our previous work.     

欺骗攻击环境下具有执行器故障的跳变耦合 信息物理系统的同步控制

针对一类离散Markov跳变耦合信息物理系统(CPS)的同步控制问题,在考虑系统参数跳变、耦合参数跳变、控制信息不完全和人为攻击的情况下,设计同步控制器实现CPS的同步.首先,给出具有随机欺骗攻击和执行器故障的Markov跳变耦合CPS模型.其次,基于矩阵Kronecker积,得到同步误差系统,将CPS的同步控制问题转化为同步误差系统的稳定性分析问题.再次,通过构造合适的Lyapunov-Krasovskii泛函,并利用Lyapunov稳定性理论和线性矩阵不等式方法得到使同步误差系统稳定的充分条件,在此基础上,设计同步控制器实现对Markov跳变耦合CPS的同步控制.最后,通过数值仿真例子说明该同步控制器设计方法的有效性.     

DoS攻击下的信息物理系统事件触发预测控制设计

针对信息物理系统(CPS)安全控制设计问题,提出拒绝服务(DoS)攻击下具有任意有界丢包的事件触发预测控制(ETPC)方法.首先,考虑DoS攻击能量的有限性及攻击行为的任意性,将DoS攻击描述为事件触发通信机制下的任意有界丢包;其次,在控制器端利用最近一次收到的状态信息进行控制器增益序列的预测设计以补偿DoS攻击造成的数据包丢失;随后,基于Lyapunov稳定性理论及切换系统分析方法考虑了DoS攻击下CPS的安全性并给出了控制序列设计方法.所提出的ETPC设计方法只需利用最近时刻收到的状态信息,无需满足传统CPS稳定性对最大允许丢包数的约束,为大时滞CPS的稳定性分析及控制提供了有效的解决方案.最后,通过仿真实例验证所提出的基于事件触发预测控制设计方法的有效性.     

离散事件系统框架下信息物理系统攻击问题综述

信息物理系统(cyber physical system, CPS)由受控对象、传感器、执行器、监控器和通信网络组成,通信网络的使用增加了信息物理系统面临外部攻击的风险.鉴于此,综述基于离散事件系统框架处理信息物理系统攻击问题的相关研究工作.首先对信息物理系统进行简要介绍;然后对信息物理系统中的攻击进行分类;最后重点阐述信息物理系统中攻击策略的设计、攻击的检测与防御以及攻击鲁棒性监控器设计的研究现状.     

网络攻击下的信息物理系统安全状态估计研究综述

近年来,信息物理系统在工业界的广泛应用引起了人们对系统安全问题的极大关注.信息物理系统对通信网络的深度依赖,使得网络攻击成为其中最为严峻的威胁之一,特别是那些能够干扰系统状态认知的攻击,因此,安全状态估计(即在遭受攻击时正确估计系统状态)已成为各界广泛关注的安全问题之一.此文旨在总结网络攻击下信息物理系统安全状态估计研究的进展.首先,介绍典型的网络攻击,并详细阐述在稀疏攻击下的安全状态估计问题.其次,探讨集中式安全状态估计和分布式安全状态估计的研究现状.在考虑稀疏攻击下安全状态估计问题的难点时,关键在于如何快速找到受到攻击的信道集合(这可能涉及到高计算复杂度).因此,将安全状态估计方法分为遍历搜索和非遍历搜索两大类,并对现有方法的优缺点进行归纳总结和详细阐述.然后,介绍稀疏攻击下信息物理系统安全状态能观性分析的研究现状.现有的研究结果表明:增加检测机制或先验知识可以缓解在稀疏攻击下安全状态估计所需的基础冗余度要求;同时,通过区分攻击和故障,也能有效降低传感器冗余度要求.最后,对信息物理系统安全状态估计仍然存在的问题进行展望,并提出一些可能的解决方向.