發(fā)布時間：2018-06-29 11:16

  本文選題：視頻傳感器網(wǎng)絡 + 一致性　； 參考：《湘潭大學》2017年碩士論文

【摘要】：基于無線傳感器網(wǎng)絡的目標跟蹤目前已應用于建筑物監(jiān)控觀測、動態(tài)定位與跟蹤、城市管理、搶險救災、國防等各個領(lǐng)域,是國內(nèi)外研究的熱點。在基于狀態(tài)估計實現(xiàn)目標跟蹤的方法中,分布式估計方法通常依賴于傳感器節(jié)點之間的點對點通信,具有穩(wěn)定性高、容錯能力強、計算代價小等優(yōu)點。在傳感器網(wǎng)絡中,傳感器節(jié)點可以獲得關(guān)于目標狀態(tài)的多個測量值,分布式估計的目的是利用網(wǎng)絡中的所有測量值來獲得關(guān)于目標狀態(tài)的精確估計,而并不需要中心節(jié)點作為信息融合中心。論文對基于視頻傳感器網(wǎng)絡的分布式目標跟蹤技術(shù)進行了深入研究,并提出了提高跟蹤精度的方法。本文提出了基于一致性算法的信息加權(quán)一致性濾波(Information-weighted Consensus Filter,ICF)算法,并將其擴展應用到非線性觀測模型。論文的主要工作如下:首先,大量查閱與研究專業(yè)相關(guān)的國內(nèi)外文獻,簡單闡述了基于無線傳感器網(wǎng)絡的目標跟蹤的研究意義及研究現(xiàn)狀。對幾種視頻傳感器網(wǎng)絡中的目標跟蹤方法的優(yōu)缺點進行了概括。其次,分析了現(xiàn)有的兩種分布式目標跟蹤方法。本文首先簡要描述了平均一致性算法的基本原理。然后闡述了目前應用較廣的兩種基于視頻傳感器網(wǎng)絡的分布式目標跟蹤方法,即卡爾曼一致性濾波(Kalman Consensus Filter,KCF)方法和廣義卡爾曼一致性濾波(Generalized Kalman Consensus Filter,GKCF)方法。重點分析了目標狀態(tài)估計實現(xiàn)過程,并從理論和仿真兩方面進行了對比分析。然后,針對視頻傳感器節(jié)點具有有限視場,傳感器網(wǎng)絡中存在無法獲得關(guān)于目標測量信息的樸素節(jié)點這一問題,提出了信息加權(quán)一致性濾波算法。首先介紹了視頻傳感器網(wǎng)絡的特性,然后基于集中式最大后驗估計推導出目標的集中式狀態(tài)估計的信息形式,接著基于一致性算法推導出目標狀態(tài)估計方程的分布式實現(xiàn),最后結(jié)合目標狀態(tài)動態(tài)模型,提出信息加權(quán)一致性濾波方法。本文還從理論上對比分析了ICF算法相較其他算法的優(yōu)勢。仿真表明,相較傳統(tǒng)的KCF算法和GKCF算法,ICF算法的跟蹤精度更高且趨于集中式目標跟蹤算法。最后,提出了基于非線性觀測模型的擴展信息加權(quán)一致性濾波(Extended Information-weighted Consensus Filter,EICF)算法。首先描述了視頻傳感器網(wǎng)絡的非線性觀測模型,然后根據(jù)擴展卡爾曼濾波算法和一致性算法,得到目標狀態(tài)估計和信息矩陣在非線性模型下的分布式形式,最后結(jié)合目標狀態(tài)動態(tài)模型提出了EICF方法。仿真結(jié)果驗證了該算法的有效性。
[Abstract]:The target tracking based on wireless sensor network has been applied to many fields, such as building monitoring and observation, dynamic location and tracking, urban management, rescue and disaster relief, national defense and so on. In the method of target tracking based on state estimation, distributed estimation usually depends on point-to-point communication between sensor nodes, which has the advantages of high stability, strong fault tolerance and low computational cost. In sensor networks, sensor nodes can obtain multiple measurements of the target state. The purpose of distributed estimation is to obtain accurate estimation of the target state using all the measurements in the network. The central node is not needed as the information fusion center. In this paper, the distributed target tracking technology based on video sensor network (VSNs) is studied, and a method to improve the tracking accuracy is proposed. In this paper, an information weighted consensus filter (ICF) algorithm based on consistency algorithm is proposed and extended to nonlinear observation model. The main work of this paper is as follows: firstly, the research significance and research status of target tracking based on wireless sensor networks are briefly described by consulting a large number of domestic and foreign literatures related to the research major. The advantages and disadvantages of several target tracking methods in video sensor networks are summarized. Secondly, two existing distributed target tracking methods are analyzed. In this paper, the basic principle of average consistency algorithm is briefly described. Then, two widely used distributed target tracking methods based on video sensor networks, Kalman consensus filter (KCF) and Generalized Kalman consensus filter (GKCF), are described. The realization process of target state estimation is analyzed, and the theoretical and simulation results are compared. Then, aiming at the problem that video sensor nodes have limited field of view and the naive nodes in sensor networks can not obtain information about target measurement, a weighted consistency filtering algorithm is proposed. This paper first introduces the characteristics of video sensor networks, then derives the information form of the centralized state estimation based on the centralized maximum a posteriori estimation, and then deduces the distributed realization of the target state estimation equation based on the consistency algorithm. Finally, combining the target state dynamic model, the information weighted consistency filtering method is proposed. The advantages of ICF algorithm compared with other algorithms are also analyzed theoretically. Simulation results show that the tracking accuracy of ICF algorithm is higher than that of traditional KCF algorithm and GKCF algorithm. Finally, an extended Information-weighted consensus filter (EICF) algorithm based on nonlinear observation model is proposed. Firstly, the nonlinear observation model of video sensor network is described, and then the distributed form of target state estimation and information matrix under nonlinear model is obtained according to extended Kalman filter algorithm and consistency algorithm. Finally, an EICF method is proposed based on the target state dynamic model. Simulation results verify the effectiveness of the algorithm.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.41;TP212

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本文編號：2081939