基于协同策略的工业无线网络分布式估计问题研究
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摘要
随着无线通信技术和网络技术的发展，具有灵活、高效、低成本、易维护等特点的工业无线网络技术作为工业信息化的关键，可以实现对工业全流程的“泛在感知”，保障现场设备正常运行，保证产品质量，提高产能。但由于工业现场环境复杂，大量无线业务共存，使用频段相互重叠，工业设备间电磁干扰问题突出，使得在网络中流通的信息准确性和通信可靠性无法得到保证。
本文以工业无线网络的信息可靠性为研究对象。工业无线网络的信息可靠性主要由节点感知的信息准确性、数据传输的可靠性以及网络部署的合理性三个方面决定。本文从节点层面和网路层面两个方面深入系统地研究了旨在提高感知准确性和传输可靠性的分布式协同估计问题以及面向估计准确性的网络部署问题。
在节点层面，针对工业现场多类传感器节点并存、节点感知数据存在误差以及不同位置的节点间数据不一致等问题，论文研究了两类传感器节点的分布式参数估计问题，提出了基于中继的工业无线网络分布式估计算法。利用Markov链及代数图论等知识，从理论上分析了算法的基本性质，得到了中继节点估计值与主节点估计值之间的关系，同时基于随机分析的框架，研究了算法的均方稳定性，建立了算法稳定的充分条件，探讨了网络拓扑对估计性能的影响。数值仿真结果很好地验证了理论分析的结论，并表明网络拓扑及相关估计参数对估计算法性能有着重要影响。
针对具有动态特性的状态的分布式估计问题，论文提出了存在两类传感器节点的工业无线网络分布式一致性Kalman滤波算法。该算法融合了Kalman滤波算法对数据的去噪功能以及分布式一致性算法对数据差异的一致化处理特性。为保证估计误差的最小化，本文采用了分离序贯设计的思想，从无偏性和最优性两个角度出发，给出了最优估计的参数设计方法，为多类传感器节点并存时的分布式估计参数设计难题提供了一种可行方案。此外，利用It随机分析理论，论文还研究了算法的均方稳定性，并得到了算法稳定的充分条件。
在网络层面，论文研究了确保分布式估计性能的的工业无线网络节点部署问题。首先，探究了分布式估计算法对网络拓扑的要求。然后，基于网络覆盖度、网络连通性以及估计性能要求，提出了考虑两类传感器节点并存情形的工业无线网络部署算法。考虑到网络部署算法生成的网络拓扑可能存在结构不合理和链接冗余等问题，论文进一步提出了网络拓扑优化算法以及估计参数调整策略。大量的数值仿真结果表明，本文提出的网络部署算法、拓扑优化算法以及估计参数调整策略能够达到对分布式估计的性能要求。
With the development of wireless technology and wireless network technology,the industrial wireless network (IWN) technology becomes the key in industrial infor-mationalization, which could ofer competitive advantages, such as greater fexibility,improved scalability, lower cost and ease of maintenance. The IWNs could enablethe industrial end-users to achieve “ubiquitous sensing” of the whole assembly line,guarantee the normal operation of the feld devices, improve the product quality andincrease the productivity and energy efciency. However, the harsh industrial environ-ments, coexistence of several wireless services, overlapping of their frequency bandsand strong electromagnetic interference severely infuence the quality of wireless com-munication such that the information accuracy and wireless reliability can not be guar-anteed.
In this dissertation, we focus on the information reliability issues of IWNs. Thenotion of reliability mainly has three facets: information accuracy of the sensor nodes,reliable data transmission, coverage and deployment of the network. In this disserta-tion, we pay our attention to the information accuracy at the sensor node level, andcoverage and deployment problem at the network level. Specifcally, we investigatethe distributed estimation problem to improve the information accuracy via sensor co-operation at the sensor nodes, and deployment problem to guarantee the estimationperformance at the network level.
At the sensor node level, since there are usually many kinds of sensor nodes co-existing in the industrial felds, moreover, the measurements of sensor nodes are cor-rupted by environment noise and there are disagreements of data among sensor nodesat diferent places, we study the problem of distributed estimation of deterministic pa-rameters in IWNs with two types of sensor nodes, namely, the primary nodes and the relay ones. A novel consensus-based distributed estimation algorithm for two types ofsensor nodes is proposed. Then we analyze its properties by using results borrowedfrom Markov chain and algebraic graph theory. The results show that the estimatesof the relay nodes are determined by those of the primary sensor nodes. Moreover,its mean-square stability is established by casting it into a stochastic framework. Thesimulation results show that the performance of the proposed algorithm is highly re-lated with the network topology, which means that we should pay much attention tothe network topology design.
Wealsostudytheproblemsofdistributedstateestimationatthesensornodelevel.Combined with the classical Kalman flter and distributed consensus algorithm, wepropose a novel distributed optimal consensus Kalman flter for IWNs with two typesof sensor nodes. This flter inherits the merits of Kalman flter, which can attenuateprocess and measurement noise, and those of consensus algorithms, which can enableall the sensor nodes to achieve agreement on their estimates. In order to minimize theestimate errors, we adopt the sequential scheme to analyze its unbiasedness and opti-mality, and presentthe designguideline ofthe optimalflters, whichprovidesafeasibleapproach to the flter design in the present of two types of sensor nodes. Moreover, itsmean-square stability is established by using It stochastic diferential theory.
Atthenetworklevel,weinvestigatethesensordeploymentproblemfordistributedestimation in IWNs. Firstly, we discuss the relation of distributed estimation and net-work topology. And we then propose a sensor deployment scheme with coverage,connectivity and estimation requirement guarantees. In order to decrease the structureirrationality and link redundance, we further present a topology optimization schemecoupled with the tuning of estimation parameters. Simulation results show that theproposed deployment algorithm and topology optimization scheme can guarantee theperformance of the corresponding distributed estimation algorithms.
本文以工业无线网络的信息可靠性为研究对象。工业无线网络的信息可靠性主要由节点感知的信息准确性、数据传输的可靠性以及网络部署的合理性三个方面决定。本文从节点层面和网路层面两个方面深入系统地研究了旨在提高感知准确性和传输可靠性的分布式协同估计问题以及面向估计准确性的网络部署问题。
在节点层面，针对工业现场多类传感器节点并存、节点感知数据存在误差以及不同位置的节点间数据不一致等问题，论文研究了两类传感器节点的分布式参数估计问题，提出了基于中继的工业无线网络分布式估计算法。利用Markov链及代数图论等知识，从理论上分析了算法的基本性质，得到了中继节点估计值与主节点估计值之间的关系，同时基于随机分析的框架，研究了算法的均方稳定性，建立了算法稳定的充分条件，探讨了网络拓扑对估计性能的影响。数值仿真结果很好地验证了理论分析的结论，并表明网络拓扑及相关估计参数对估计算法性能有着重要影响。
针对具有动态特性的状态的分布式估计问题，论文提出了存在两类传感器节点的工业无线网络分布式一致性Kalman滤波算法。该算法融合了Kalman滤波算法对数据的去噪功能以及分布式一致性算法对数据差异的一致化处理特性。为保证估计误差的最小化，本文采用了分离序贯设计的思想，从无偏性和最优性两个角度出发，给出了最优估计的参数设计方法，为多类传感器节点并存时的分布式估计参数设计难题提供了一种可行方案。此外，利用It随机分析理论，论文还研究了算法的均方稳定性，并得到了算法稳定的充分条件。
在网络层面，论文研究了确保分布式估计性能的的工业无线网络节点部署问题。首先，探究了分布式估计算法对网络拓扑的要求。然后，基于网络覆盖度、网络连通性以及估计性能要求，提出了考虑两类传感器节点并存情形的工业无线网络部署算法。考虑到网络部署算法生成的网络拓扑可能存在结构不合理和链接冗余等问题，论文进一步提出了网络拓扑优化算法以及估计参数调整策略。大量的数值仿真结果表明，本文提出的网络部署算法、拓扑优化算法以及估计参数调整策略能够达到对分布式估计的性能要求。
With the development of wireless technology and wireless network technology,the industrial wireless network (IWN) technology becomes the key in industrial infor-mationalization, which could ofer competitive advantages, such as greater fexibility,improved scalability, lower cost and ease of maintenance. The IWNs could enablethe industrial end-users to achieve “ubiquitous sensing” of the whole assembly line,guarantee the normal operation of the feld devices, improve the product quality andincrease the productivity and energy efciency. However, the harsh industrial environ-ments, coexistence of several wireless services, overlapping of their frequency bandsand strong electromagnetic interference severely infuence the quality of wireless com-munication such that the information accuracy and wireless reliability can not be guar-anteed.
In this dissertation, we focus on the information reliability issues of IWNs. Thenotion of reliability mainly has three facets: information accuracy of the sensor nodes,reliable data transmission, coverage and deployment of the network. In this disserta-tion, we pay our attention to the information accuracy at the sensor node level, andcoverage and deployment problem at the network level. Specifcally, we investigatethe distributed estimation problem to improve the information accuracy via sensor co-operation at the sensor nodes, and deployment problem to guarantee the estimationperformance at the network level.
At the sensor node level, since there are usually many kinds of sensor nodes co-existing in the industrial felds, moreover, the measurements of sensor nodes are cor-rupted by environment noise and there are disagreements of data among sensor nodesat diferent places, we study the problem of distributed estimation of deterministic pa-rameters in IWNs with two types of sensor nodes, namely, the primary nodes and the relay ones. A novel consensus-based distributed estimation algorithm for two types ofsensor nodes is proposed. Then we analyze its properties by using results borrowedfrom Markov chain and algebraic graph theory. The results show that the estimatesof the relay nodes are determined by those of the primary sensor nodes. Moreover,its mean-square stability is established by casting it into a stochastic framework. Thesimulation results show that the performance of the proposed algorithm is highly re-lated with the network topology, which means that we should pay much attention tothe network topology design.
Wealsostudytheproblemsofdistributedstateestimationatthesensornodelevel.Combined with the classical Kalman flter and distributed consensus algorithm, wepropose a novel distributed optimal consensus Kalman flter for IWNs with two typesof sensor nodes. This flter inherits the merits of Kalman flter, which can attenuateprocess and measurement noise, and those of consensus algorithms, which can enableall the sensor nodes to achieve agreement on their estimates. In order to minimize theestimate errors, we adopt the sequential scheme to analyze its unbiasedness and opti-mality, and presentthe designguideline ofthe optimalflters, whichprovidesafeasibleapproach to the flter design in the present of two types of sensor nodes. Moreover, itsmean-square stability is established by using It stochastic diferential theory.
Atthenetworklevel,weinvestigatethesensordeploymentproblemfordistributedestimation in IWNs. Firstly, we discuss the relation of distributed estimation and net-work topology. And we then propose a sensor deployment scheme with coverage,connectivity and estimation requirement guarantees. In order to decrease the structureirrationality and link redundance, we further present a topology optimization schemecoupled with the tuning of estimation parameters. Simulation results show that theproposed deployment algorithm and topology optimization scheme can guarantee theperformance of the corresponding distributed estimation algorithms.
引文
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