【摘要】：隨著微機(jī)電系統(tǒng)技術(shù)、無線通信技術(shù)、計算技術(shù)與傳感器技術(shù)的迅猛發(fā)展,集信息感知、信息處理與信息傳輸于一體的多學(xué)科交叉技術(shù)——無線傳感器網(wǎng)絡(luò)——成為國際上備受關(guān)注的前沿?zé)狳c研究領(lǐng)域。因其獨有的特點,無線傳感器網(wǎng)絡(luò)被廣泛應(yīng)用于國防軍事、環(huán)境監(jiān)測、智能家居與醫(yī)療護(hù)理等多個領(lǐng)域,被認(rèn)為是21世紀(jì)最有影響的21項技術(shù)之一和改變世界的10大技術(shù)之一。 移動目標(biāo)檢測與跟蹤作為無線傳感器網(wǎng)絡(luò)的重點應(yīng)用領(lǐng)域之一,有著廣泛的前景,尤其在安全防衛(wèi)領(lǐng)域發(fā)揮著重大作用。本文考慮無線傳感器網(wǎng)絡(luò)大規(guī)模和資源受限的特點,針對目標(biāo)檢測與跟蹤,研究網(wǎng)絡(luò)節(jié)點部署、節(jié)點跟蹤調(diào)度與信息的實時匯報問題,并通過仿真與實際系統(tǒng)驗證算法的有效性。本文的主要貢獻(xiàn)有如下幾個方面： 1.針對靜態(tài)傳感器網(wǎng)絡(luò)隨機(jī)部署后難以有效形成柵欄覆蓋的問題,提出了利用移動節(jié)點再部署以彌補(bǔ)靜態(tài)節(jié)點間漏洞,從而形成柵欄覆蓋。提出了加權(quán)柵欄圖的概念,將最小代價移動節(jié)點問題轉(zhuǎn)化為加權(quán)柵欄圖的最短路徑問題；將最小能耗柵欄覆蓋構(gòu)建問題映射為最小成本二部分配問題；在此基礎(chǔ)上,提出了有向柵欄覆蓋構(gòu)建算法以最小的代價部署和調(diào)度移動節(jié)點與靜態(tài)節(jié)點形成柵欄覆蓋。理論分析和仿真結(jié)果驗證了算法的有效性。 2.從實際角度出發(fā),首次考慮節(jié)點位置誤差存在對柵欄覆蓋的影響。理論分析了節(jié)點位置誤差如何影響所需的真實移動節(jié)點數(shù)目,并提出了最優(yōu)的遞進(jìn)式移動節(jié)點部署算法指導(dǎo)節(jié)點移動彌補(bǔ)靜態(tài)節(jié)點間漏洞。此外,考慮位置誤差的影響,提出了容錯加權(quán)柵欄圖,從圖論角度得到確保柵欄覆蓋構(gòu)建所需的最少移動節(jié)點數(shù)目。實驗結(jié)果驗證了誤差的影響以及理論分析和算法的正確性。 3.針對大規(guī)模網(wǎng)絡(luò)中移動目標(biāo)有效跟蹤及位置實時匯報問題,提出了基于簇的目標(biāo)跟蹤算法。提出利用簇頭喚醒和休眠節(jié)點跟蹤目標(biāo),并通過簇頭構(gòu)建的骨干網(wǎng)實時匯報目標(biāo)位置。實驗結(jié)果表明,基于簇的目標(biāo)跟蹤算法在準(zhǔn)確跟蹤目標(biāo)的同時,不僅節(jié)省了能耗,也減少了位置匯報的延時。 4.針對基于簇的目標(biāo)跟蹤算法所面臨的邊界問題,將動態(tài)簇思想融入到靜態(tài)簇網(wǎng)絡(luò)結(jié)構(gòu)中,提出了基于混合簇的目標(biāo)跟蹤算法。在目標(biāo)移動過程,不斷的利用靜態(tài)簇與動態(tài)簇輪流跟蹤目標(biāo)。實驗結(jié)果表明,基于混合簇的目標(biāo)跟蹤算法有效解決了基于簇的跟蹤算法所面臨的邊界問題,并在性能方面優(yōu)于其他經(jīng)典的目標(biāo)跟蹤算法。 5.針對已有目標(biāo)跟蹤算法缺乏系統(tǒng)驗證的問題,在基于簇的目標(biāo)算法框架下,設(shè)計實現(xiàn)了真正的移動目標(biāo)跟蹤系統(tǒng)。此系統(tǒng)包含36個傳感器節(jié)點,一個匯聚節(jié)點與一個基站。多次的靜止目標(biāo)定位實驗與移動目標(biāo)跟蹤實驗,充分驗證了目標(biāo)跟蹤系統(tǒng)的有效性。
[Abstract]:With the rapid development of MEMS technology, wireless communication technology, computing technology and sensor technology, collecting information perception, Wireless sensor network (WSN), a multidisciplinary and integrated information processing and information transmission technology, has become a hot research field in the world. Because of its unique characteristics, wireless sensor network is widely used in many fields, such as national defense, environment monitoring, intelligent home and medical care, etc. It is considered to be one of the 21 most influential technologies in the 21st century and one of the top 10 technologies to change the world. As one of the key application fields of wireless sensor networks, mobile target detection and tracking has a wide range of prospects, especially in the field of security and defense plays an important role. Considering the characteristics of large scale and limited resources in wireless sensor networks, this paper studies the deployment of network nodes, the scheduling of nodes and the real-time reporting of information for target detection and tracking. The effectiveness of the algorithm is verified by simulation and practical system. The main contributions of this paper are as follows: 1. In order to solve the problem that it is difficult for static sensor networks to effectively form fence coverage after random deployment, this paper proposes to use mobile nodes to redeploy to make up for the loopholes between static nodes, so as to form fence coverage. In this paper, the concept of weighted palisade graph is proposed, the minimum cost moving node problem is transformed into the shortest path problem of weighted palisade graph, and the minimum energy consumption fence cover construction problem is mapped to the minimum cost bipartite assignment problem. On this basis, a directed fence coverage construction algorithm is proposed to deploy and schedule moving nodes and static nodes to form fence overlay at the minimum cost. Theoretical analysis and simulation results verify the effectiveness of the algorithm. 2. From the practical point of view, the effect of node position error on fence coverage is considered for the first time. This paper theoretically analyzes how node position error affects the number of real mobile nodes, and proposes an optimal progressive mobile node deployment algorithm to guide node movement to make up for the static node loophole. In addition, considering the influence of position error, a fault-tolerant weighted palisade graph is proposed, and the minimum number of moving nodes needed to ensure the construction of fence coverage is obtained from the point of view of graph theory. The experimental results verify the influence of error and the correctness of theoretical analysis and algorithm. 3. Aiming at the problem of effective tracking and real time reporting of moving targets in large scale networks, a clust-based target tracking algorithm is proposed. A cluster head wake-up and dormant node is proposed to track the target, and a backbone network constructed by cluster head is used to report the target position in real time. The experimental results show that the cluster-based target tracking algorithm not only saves energy consumption but also reduces the time delay of location reporting. 4. Aiming at the boundary problem faced by the cluster-based target tracking algorithm, the idea of dynamic cluster is integrated into the static cluster network structure, and a hybrid cluster-based target tracking algorithm is proposed. In the process of moving the target, the static cluster and the dynamic cluster are used to track the target in turn. Experimental results show that the target tracking algorithm based on mixed clusters can effectively solve the boundary problem faced by the cluster-based tracking algorithm and is superior to other classical target tracking algorithms in performance. 5. In view of the lack of system verification of existing target tracking algorithms, a real moving target tracking system is designed and implemented under the framework of cluster-based target algorithms. The system consists of 36 sensor nodes, a convergent node and a base station. The effectiveness of the target tracking system is fully verified by many static target localization experiments and moving target tracking experiments.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2014
【分類號】：TN929.5;TP212.9

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 孫其博;劉杰;黎，

本文編號：2352897