本文選題：分布式估計(jì) + 無線傳感器網(wǎng)絡(luò)��； 參考：《西南大學(xué)》2017年碩士論文

【摘要】：分布式估計(jì)的目的是給定一個(gè)觀測(cè)序列,網(wǎng)絡(luò)中的節(jié)點(diǎn)通過合作的方式來估計(jì)一個(gè)隨機(jī)或者確定性的參數(shù)。由于分布式估計(jì)算法的穩(wěn)定性、魯棒性和節(jié)能性等特點(diǎn),使得其在無線傳感器網(wǎng)絡(luò)中非常實(shí)用。分布式估計(jì)主要有三種信息交換策略,擴(kuò)散策略、一致性策略及增量策略,其中擴(kuò)散策略的估計(jì)性能最優(yōu)。無線傳感器網(wǎng)絡(luò)(WSN)在不同的空間地點(diǎn)采集觀測(cè)數(shù)據(jù),可以獲得更大的平均信噪比,通過分布式處理大量的采集信息能夠提高估計(jì)的精確度,提高魯棒性,而且其拓?fù)浣Y(jié)構(gòu)的獨(dú)特的特點(diǎn)對(duì)于許多應(yīng)用有非常重要的意義。無線傳感器網(wǎng)絡(luò)的結(jié)構(gòu)是分布式估計(jì)的基礎(chǔ),分布式估計(jì)算法是分布式估計(jì)的核心,因此,將網(wǎng)絡(luò)結(jié)構(gòu)與分布式估計(jì)算法有效的結(jié)合起來,會(huì)更有效的解決分布式參數(shù)估計(jì)問題。擴(kuò)散最小均方算法(DLMS)是典型的分布式估計(jì)方法,由于DLMS具備結(jié)構(gòu)簡(jiǎn)單、易于實(shí)現(xiàn)、性能穩(wěn)定、魯棒性強(qiáng)等特點(diǎn),使得DLMS算法的應(yīng)用較為廣泛。然而DLMS算法也存在缺點(diǎn),網(wǎng)絡(luò)中的節(jié)點(diǎn)都要接收和發(fā)送數(shù)據(jù)直接給與自己相連的鄰居節(jié)點(diǎn),那么節(jié)點(diǎn)間總的通信量會(huì)有負(fù)擔(dān)。本文首先探究了表征WSN網(wǎng)絡(luò)局部結(jié)構(gòu)的特征量——模體(包括三節(jié)點(diǎn)模體和四節(jié)點(diǎn)模體)對(duì)DLMS算法性能的影響,發(fā)現(xiàn)DLMS算法的性能與網(wǎng)絡(luò)中帶有閉合三角行模體數(shù)量有一定關(guān)系。進(jìn)而針對(duì)DLMS算法節(jié)點(diǎn)間通信負(fù)擔(dān)重的問題,提出了打破模體擴(kuò)散最小均方算法,此算法大大減少了節(jié)點(diǎn)間的通信負(fù)載,且算法估計(jì)性能損失較小,更好的達(dá)到了通信負(fù)載與估計(jì)性能的均衡,這個(gè)研究對(duì)于節(jié)約網(wǎng)絡(luò)能量和帶寬有重要作用。本文還首次將擴(kuò)散策略應(yīng)用到相位估計(jì)中,結(jié)合交替迭代最小化方法,提出了基于傳感器網(wǎng)絡(luò)的分布式相位估計(jì)算法,提出的算法能更好的抗擊噪聲的干擾。接著本文從網(wǎng)絡(luò)的整體結(jié)構(gòu)出發(fā),探究了WSN中不同的網(wǎng)絡(luò)模型,包括規(guī)則網(wǎng)絡(luò)、小世界網(wǎng)路、隨機(jī)網(wǎng)絡(luò)和無標(biāo)度網(wǎng)絡(luò),對(duì)提出的分布式相位估計(jì)算法性能的影響。發(fā)現(xiàn)采用不同的網(wǎng)絡(luò)模型,得到的算法性能有較大差異,這個(gè)研究對(duì)于分布式參數(shù)估計(jì)問題中傳感器網(wǎng)絡(luò)的拓?fù)浣Y(jié)構(gòu)設(shè)計(jì)有一定指導(dǎo)作用。
[Abstract]:The purpose of distributed estimation is to estimate a random or deterministic parameter by means of cooperation between nodes in the network given an observation sequence. Because of the stability, robustness and energy saving of the distributed estimation algorithm, it is very practical in wireless sensor networks. There are three kinds of information exchange strategy, diffusion strategy, consistency strategy and incremental strategy, among which the estimation performance of diffusion strategy is optimal. Wireless sensor networks (WSN) can obtain greater average SNR by collecting observation data at different spatial locations. The estimation accuracy and robustness can be improved by distributed processing of a large amount of collected information. Moreover, the unique characteristics of its topology are of great significance to many applications. The structure of wireless sensor networks is the basis of distributed estimation, and the distributed estimation algorithm is the core of distributed estimation. It can solve the problem of distributed parameter estimation more effectively. Diffusion least mean square algorithm (DLMS) is a typical distributed estimation method. DLMS is widely used because of its simple structure, easy implementation, stable performance and strong robustness. However the DLMS algorithm also has its shortcomings. The nodes in the network have to receive and send data directly to their neighbor nodes so the total traffic between the nodes will have a burden. In this paper, the influence of characteristic motifs (including three-node motifs and four-node motifs) that characterize the local structure of WSN networks on the performance of DLMS algorithm is investigated. It is found that the performance of DLMS algorithm is related to the number of closed triangular row motifs in the network. Then, aiming at the problem of heavy communication burden between nodes in DLMS algorithm, a new algorithm of breaking mode-diffusion minimum mean-square algorithm is proposed. This algorithm greatly reduces the communication load between nodes, and the estimation performance loss of the algorithm is relatively small. Better balance of communication load and estimation performance is achieved, which plays an important role in saving network energy and bandwidth. This paper also applies diffusion strategy to phase estimation for the first time, and proposes a distributed phase estimation algorithm based on sensor networks combined with alternating iterative minimization method. The proposed algorithm can better resist noise interference. Then, from the overall structure of the network, this paper explores the influence of different network models in WSN, including regular network, small-world network, random network and scale-free network, on the performance of the proposed distributed phase estimation algorithm. It is found that the performance of the proposed algorithm is different with different network models. This study can be used to guide the topology design of sensor networks in distributed parameter estimation problems.
【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5

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本文編號(hào)：2112155