Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises

In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis.     

Distributed consensus estimation for diffusion systems with missing measurements over sensor networks

In this paper, the problem of distributed consensus estimation with randomly missing measurements is investigated for a diffusion system over the sensor network. A random variable, the probability of which is known a priori, is used to model the randomly missing phenomena for each sensor. The aim of the addressed estimation problem is to design distributed consensus estimators depending on the neighbouring information such that, for all random measurement missing, the estimation error systems are guaranteed to be globally asymptotically stable in the mean square. By using Lyapunov functional method and the stochastic analysis approach, the sufficient conditions are derived for the convergence of the estimation error systems. Finally, a numerical example is given to demonstrate the effectiveness of the proposed distributed consensus estimator design scheme.     

Reusable coordinator modules for massively concurrent applications

Isolating computation and communication concerns into separate pure computation and pure coordination modules enhances modularity, understandability and reusability of parallel and/or distributed software. MANIFOLD is a pure coordination language that encourages this separation. We use real, concrete, running ANIFOLD programs to demonstrate the concept of pure coordination modules and the advantage of their reuse in applications of different natures. Performance results for the examples presented in this paper show that the overhead of using MANIFOLD to achieve this enhanced modularity and reusability is in practice small, compared to the more conventional paradigms for the design and programming of parallel and distributed software. © 1998 John Wiley & Sons, Ltd.     

Real-Time Atomic Multicast Algorithms Implemented on a Shared Memory Multiprocessor

Processes are distributed over processors that share directly accessible memory. Processes exchange messages via memory. Algorithms are specified which satisfy the atomic multicast requirements under two fault hypotheses: (1) fail-stop memory and fail stop processes and (2) fail-stop memory and processes with timing failures. It is shown that the algorithms can be applied under different scheduling conditions: (1) hard real-time (HRT) applications composed of periodically scheduled tasks and (2) HRT applications with tasks scheduled according to a static off-line calculated schedule. The performance measurements on an implementation of two algorithms are presented.     

七号信令网络监测系统设计

针对电信管理网建设需要,开发了移动通信七号信令网络监测系统.系统采用中心站和远端站进行远程分布式数据采集和分布式多层次数据处理,实现七号信令网监测的各种实时或综合应用功能.得益于这种分布式系统结构以及分层数据处理方式,系统在实际运行中表现出了良好的性能.     

state estimation with fading measurements,randomly varying nonlinearities and probabilistic distributed delays

In this paper, the state estimation problem is investigated for a class of discrete‐time stochastic systems in simultaneous presence of three network‐induced phenomena, namely, fading measurements, randomly varying nonlinearities and probabilistic distributed delays. The channel fading is characterized by the ?th‐order Rice fading model whose coefficients are mutually independent random variables with given probability density functions. Two sequences of random variables obeying the Bernoulli distribution are utilized to govern the randomly varying nonlinearities and probabilistic distributed delays. The purpose of the problem addressed is to design an state estimator such that the dynamics of the estimation errors is stochastically stable and the prespecified disturbance rejection attenuation level is guaranteed. Through intensive stochastic analysis, sufficient conditions are established under which the addressed state estimation problem is recast as a convex optimization one that can be solved via the semi‐definite program method. Finally, a simulation example is provided to show the usefulness of the proposed state estimation scheme. Copyright © 2014 John Wiley & Sons, Ltd.     

Recursive Approximation of Complex Behaviours With IoT-Data Imperfections

This paper presents an approach to recursively estimate the simplest linear model that approximates the time-varying local behaviors from imperfect(noisy and incomplete) measurements in the internet of things(IoT) based distributed decision-making problems. We first show that the problem of finding the lowest order model for a multi-input single-output system is a cardinality(l0) optimization problem, known to be NP-hard.To solve the problem a simpler approach is proposed which uses the recently developed atomic norm concept and the modified Frank-Wolfe(mFW) algorithm is introduced. Further, the paper computes the minimum data-rate required for computing the models with imperfect measurements. The proposed approach is illustrated on a building heating, ventilation, and air-conditioning(HVAC) control system that aims at optimizing energy consumption in commercial buildings using IoT devices in a distributed manner. The HVAC control application requires recursive thermal dynamical model updates due to frequently changing conditions and non-linear dynamics. We show that the method proposed in this paper can approximate such complex dynamics on single-board computers interfaced to sensors using unreliable communication channels. Real-time experiments on HVAC systems and simulation studies are used to illustrate the proposed method.     

Real‐time Kalman filtering based on distributed measurements

A kind of real‐time Kalman filtering problem is discussed for systems with distributed multichannel measurements. Recursive filters are presented for two cases with correlated and uncorrelated measurement noises. An optimal algorithm is constructed using projection theory in Hilbert space according to a first‐come‐first‐served scheme. An update is generated whenever a new measurement arrives at a central unit. Therefore, the algorithm has the practical advantages of flexibility and the easiness for real‐time implementation. Copyright © 2012 John Wiley & Sons, Ltd.     

基于间歇测量的状态估计稳定性和精度研究*

针对分布式控制系统状态估计过程出现的测量值丢失问题,本文量化分析测量值丢失对估计稳定性和精度的影响。首先,根据随机测量模型重新推导带测量值丢失的卡尔曼滤波器,得到估计误差协方差迭代式。然后,将随机误差协方差迭代式建模为修正的黎卡提微分方程,提出了估计稳定性和精度分析方法。最后,列举不同的系统实例,证明临界包到达率的存在性,当测量值到达率大于临界值时,平均误差协方差从发散过渡到有界,进而得到估计精度与包达率之间的函数关系。     

SLA-based complementary approach for network intrusion detection

Enhancing the intrusion detection system is essential to maintain user confidence in network services security. However, the threat of intruders on Internet services is prevalent. This paper proposes a distributed edge-to-edge complementary approach for intrusion detection in a DiffServ/MPLS domain. The QoS metrics are inspected at the edges routers to determine anomalous behavior in the network traffic. Consumed ratios of one-way delay variation (OWDV) and packet loss are computed to monitor service level agreement (SLA) violations. The bandwidth ratio is measured to differentiate abnormal from normal traffic as well as to detect multiple intrusions launched simultaneously. We employed SLA as a comparison scale to infer the deviation between the users consumed ratios and the predefined ratios in the SLA. Service violation occurs and intrusion may be launched when the predefined ratios are exceeded. The complementary services of DiffServ and MPLS techniques guarantee accurate measurements, whereas the complementary measurements of active and passive techniques immunize network performance against scalability limitation. Simulation results indicate that the proposed approach is capable of monitoring SLA violations and can filter out traffic of intruders who breach SLA without disturbing the normal traffic of legitimate users.     

Distributed control of target cooperative encirclement and tracking using range-based measurements

This paper investigates a distributed coordinate-free control method of ground target cooperative encirclement and tracking for an unmanned aerial vehicle (UAV) formation. Different from the existing methods, the relevant information for the circumnavigation can be measured by the UAV sensors rather than directly from the tracked target. Based on these distance measurements, a multi-UAV encirclement guidance law that satisfies both target coordination and tracking is designed. According to the proposed lemma of interconnected system, the formation system under the guidance law is globally uniformly asymptotically stable. The effectiveness and advantages of the proposed controller are validated via simulations.     

Quantised polynomial filtering for nonlinear systems with missing measurements

This paper is concerned with the polynomial filtering problem for a class of nonlinear systems with quantisations and missing measurements. The nonlinear functions are approximated with polynomials of a chosen degree and the approximation errors are described as low-order polynomial terms with norm-bounded coefficients. The transmitted outputs are quantised by a logarithmic quantiser and are also subject to randomly missing measurements governed by a Bernoulli distributed sequence taking values on 0 or 1. Dedicated efforts are made to derive an upper bound of the filtering error covariance in the simultaneous presence of the polynomial approximation errors, the quantisations as well as the missing measurements at each time instant. Such an upper bound is then minimised through designing a suitable filter gain by solving a set of matrix equations. The filter design algorithm is recursive and therefore applicable for online computation. An illustrative example is exploited to show the effectiveness of the proposed algorithm.     

基于无线网络的空调控制系统设计

将无线网络技术、红外遥控技术应用于空调控制系统，完成空调的远程控制，实现真正实时的分散控制、集中管理。本文给出了空调控制系统的整体结构，设计了用于现场测控的无线网络节点，详细介绍了节点系统的软硬件实现。     

Dissipative control for state-saturated discrete time-varying systems with randomly occurring nonlinearities and missing measurements

In this paper, the dissipative control problem is investigated for a class of discrete time-varying systems with simultaneous presence of state saturations, randomly occurring nonlinearities as well as multiple missing measurements. In order to render more practical significance of the system model, some Bernoulli distributed white sequences with known conditional probabilities are adopted to describe the phenomena of the randomly occurring nonlinearities and the multiple missing measurements. The purpose of the addressed problem is to design a time-varying output-feedback controller such that the dissipativity performance index is guaranteed over a given finite-horizon. By introducing a free matrix with its infinity norm less than or equal to 1, the system state is bounded by a convex hull so that some sufficient conditions can be obtained in the form of recursive nonlinear matrix inequalities. A novel controller design algorithm is then developed to deal with the recursive nonlinear matrix inequalities. Furthermore, the obtained results are extended to the case when the state saturation is partial. Two numerical simulation examples are provided to demonstrate the effectiveness and applicability of the proposed controller design approach.     

具有测量数据丢失的网络化系统的故障检测滤波

研究一类具有测量数据部分丢失的网络化系统的故障检测滤波器设计问题. 测量数据的丢失假设满足已知概率的Bernoulli分布. 设计一个故障检测滤波器, 使残差误差系统均方指数稳定且具有一定的扰动衰减水平. 采用线性矩阵不等式方法, 给出了故障检测滤波器存在的充分条件. 最后通过例子说明该方法的有效性.     

Robust distributed H∞ filtering over an uncertain sensor network with multiple fading measurements and varying sensor delays

In this paper, the problem of robust distributed H_∞ filtering is investigated for state‐delayed discrete‐time linear systems over a sensor network with multiple fading measurements, random time‐varying communication delays, and norm‐bounded uncertainties in all matrices of the system. The diagonal matrices, whose elements are individual independent random variables, are utilized to describe the multiple fading measurements. Furthermore, the Bernoulli‐distributed white sequences are introduced to model the random occurrence of time‐varying communication delays. In the proposed filtering approach, the stability of the estimation error system is first shown by the Lyapunov stability theory and the H_∞ performance is then achieved using a linear matrix inequality method. Finally, two numerical examples are given to show the effectiveness and performance of the proposed approach.     

QoE-driven in-network optimization for Adaptive Video Streaming based on packet sampling measurements

HTTP Adaptive Streaming (HAS) is becoming the de-facto standard for adaptive streaming solutions. In HAS, a video is temporally split into segments which are encoded at different quality rates. The client can then autonomously decide, based on the current buffer filling and network conditions, which quality representation it will download. Each of these players strives to optimize their individual quality, which leads to bandwidth competition, causing quality oscillations and buffer starvations. This article proposes a solution to alleviate these problems by deploying in-network quality optimization agents, which monitor the available throughput using sampling-based measurement techniques and optimize the quality of each client, based on a HAS Quality of Experience (QoE) metric. This in-network optimization is achieved by solving a linear optimization problem both using centralized as well as distributed algorithms. The proposed hybrid QoE-driven approach allows the client to take into account the in-network decisions during the rate adaptation process, while still keeping the ability to react to sudden bandwidth fluctuations in the local network. The proposed approach allows improving existing autonomous quality selection heuristics by at least 30%, while outperforming an in-network approach using purely bitrate-driven optimization by up to 19%.     

Real-time optimal filtering for stochastic systems with multiresolutional measurements

A real-time optimal filtering algorithm for stochastic systems with multiresolutional measurements is derived. The algorithm gives fused estimates based upon all available data at a particular time index. A multiresolutional distributed filtering scheme is employed. The wavelet transform is utilized as a bridge, effectively linking different resolution levels. A tree-like hierarchical data structure introduced in this paper facilitates the real-time multiresolutional filtering.     

The improved technique of electric and magnetic parameters measurements of powdered materials

This paper presents the measurement technique that allows to determine the relative permittivity and permeability of powdered materials. Measurements are realized in a coaxial transmission line which guarantees the broad band frequency characterization. Calculations utilize the scattering matrix parameters of the two-port formed by the sample of powdered material supported by two dielectric walls. The proposed measurement procedure is demonstrated in the example of ferrite powder - Yiitrium Garnet YIG class ferrite for which the relative permittivity (??^′,??^″) and permeability (??^′,??^″) are determined in the frequency range of 200-1200 MHz.