【摘要】：估計(jì)以及檢測(cè)理論是統(tǒng)計(jì)信號(hào)處理的兩大分支,它們?cè)谟糜谔崛⌒畔⒌碾娮有盘?hào)處理系統(tǒng)中應(yīng)用廣泛,這些系統(tǒng)包括雷達(dá)、通信、聲吶、語(yǔ)音、圖像處理、生物醫(yī)學(xué)、環(huán)境監(jiān)測(cè)以及地震學(xué)等。其中檢測(cè)理論是要確定感興趣的事件是否發(fā)生或者當(dāng)前環(huán)境處于哪種離散狀態(tài),估計(jì)理論則對(duì)感興趣的事件提取出更詳細(xì)的信息。隨著傳感技術(shù)、無(wú)線網(wǎng)絡(luò)以及嵌入式系統(tǒng)等領(lǐng)域的快速發(fā)展,無(wú)線傳感器網(wǎng)絡(luò)(Wireless Sensor Networks, WSNs)在軍事以及民用的各個(gè)領(lǐng)域發(fā)揮著越來(lái)越重要的作用。傳感器節(jié)點(diǎn)被布放在目標(biāo)區(qū)域內(nèi)來(lái)獲取感興趣的信息,并被用于目標(biāo)跟蹤、環(huán)境監(jiān)測(cè)、信息安全等場(chǎng)景,這些也都是估計(jì)及檢測(cè)理論的應(yīng)用范疇。在無(wú)線傳感器網(wǎng)絡(luò)中,傳感器節(jié)點(diǎn)的能量通常是有限的,設(shè)計(jì)算法時(shí)要進(jìn)行性能與能量消耗的折中。如何在保證性能的同時(shí)盡可能減小傳感器網(wǎng)絡(luò)的能量消耗,或在給定能量及帶寬限制下最大化性能成為一個(gè)重要的問(wèn)題。本文主要針對(duì)無(wú)線傳感器網(wǎng)絡(luò)中進(jìn)行分布式目標(biāo)跟蹤以及狀態(tài)檢測(cè)的應(yīng)用,研究如何在保證性能的同時(shí)盡可能減少網(wǎng)絡(luò)的通信量,從而減少傳感器節(jié)點(diǎn)的能量消耗,提高網(wǎng)絡(luò)壽命。其中目標(biāo)跟蹤是一個(gè)典型的估計(jì)問(wèn)題,需要估計(jì)目標(biāo)每一時(shí)刻的位置以及速度；狀態(tài)檢測(cè)則用來(lái)確定當(dāng)前環(huán)境處于哪種狀態(tài),是一個(gè)檢測(cè)問(wèn)題。在利用無(wú)線傳感器網(wǎng)絡(luò)進(jìn)行目標(biāo)跟蹤時(shí),每個(gè)傳感器節(jié)點(diǎn)的數(shù)據(jù)蘊(yùn)含的信息是各不相同的。有些節(jié)點(diǎn)的數(shù)據(jù)可能蘊(yùn)含的信息很少,對(duì)于提高跟蹤性能幾乎沒(méi)有幫助,因此有必要在跟蹤過(guò)程中規(guī)劃參與目標(biāo)跟蹤的節(jié)點(diǎn)集和參與方式。本文分別提出了基于領(lǐng)導(dǎo)節(jié)點(diǎn)的目標(biāo)跟蹤算法和完全分布式的目標(biāo)跟蹤算法。在基于領(lǐng)導(dǎo)節(jié)點(diǎn)的目標(biāo)跟蹤算法中,綜合考慮了數(shù)據(jù)收集過(guò)程以及領(lǐng)導(dǎo)節(jié)點(diǎn)遷移產(chǎn)生的通信開(kāi)銷,根據(jù)跟蹤過(guò)程中的誤差矩陣進(jìn)行節(jié)點(diǎn)規(guī)劃,以最大化目標(biāo)跟蹤的性能。求解中采用Gauss-Seidel迭代以及凸松弛等手段,使得復(fù)雜的優(yōu)化問(wèn)題能夠得到快速求解。仿真結(jié)果表明,本算法在相同的通信能量約束下能夠達(dá)到更好的跟蹤性能。完全分布式的目標(biāo)跟蹤算法通常在每步跟蹤過(guò)程中利用一致性算法(Consensus Algorithm)在全網(wǎng)傳播局部信息,從而使每個(gè)節(jié)點(diǎn)得到全局或接近全局的跟蹤性能。如前所述,有些傳感器節(jié)點(diǎn)的數(shù)據(jù)對(duì)于提高跟蹤性能幾乎沒(méi)有幫助,參與一致性迭代只會(huì)浪費(fèi)能量并降低一致性算法的收斂速度。本文將節(jié)點(diǎn)規(guī)劃策略引入到分布式目標(biāo)跟蹤算法中,每次只選擇具有高信息量的節(jié)點(diǎn)參與一致性計(jì)算,從而在盡量保持跟蹤性能的同時(shí)減少網(wǎng)絡(luò)的通信開(kāi)銷。在分布式狀態(tài)檢測(cè)問(wèn)題中,每個(gè)節(jié)點(diǎn)不僅依靠自身的觀測(cè)數(shù)據(jù),還通過(guò)與鄰居的信息交互進(jìn)行檢測(cè)。然而在鄰居間直接傳遞觀測(cè)數(shù)據(jù)通信量較大。本文將社會(huì)學(xué)習(xí)機(jī)制應(yīng)用到分布式狀態(tài)檢測(cè)中,提出了一種基于私有數(shù)據(jù)和鄰居決策的分布式狀態(tài)檢測(cè)算法,并分析了算法的收斂性。由于鄰居間只傳遞對(duì)于環(huán)境狀態(tài)的決策,相比于直接交換觀測(cè)數(shù)據(jù)或似然比等連續(xù)量,可以顯著減少網(wǎng)絡(luò)通信量,當(dāng)環(huán)境的狀態(tài)空間為二元時(shí),鄰居間的信息交換小到只需要1bit。仿真結(jié)果表明,在相同的通信開(kāi)銷下,本文算法可以更快收斂到環(huán)境的真實(shí)狀態(tài)。
[Abstract]:Estimates and detection theories are two major branches of statistical signal processing, which are widely used in electronic signal processing systems for extracting information, including radar, communication, navigation, voice, image processing, biomedicine, environmental monitoring, and seismology. wherein the detection theory is to determine whether the event of interest occurs or which discrete state of the current environment, and the estimation theory extracts more detailed information for the event of interest. With the rapid development of sensor technology, wireless network and embedded system, Wireless Sensor Networks (WSNs) plays an increasingly important role in military and civil fields. The sensor nodes are placed in the target area to acquire the information of interest, and are used for target tracking, environmental monitoring, information security and other scenarios, which are also applied to the estimation and detection theory. In a wireless sensor network, the energy of the sensor node is generally limited, and a compromise between performance and energy consumption is to be performed when designing the algorithm. How to minimize energy consumption of sensor networks while ensuring performance, or maximize performance under given energy and bandwidth constraints becomes an important issue. This paper mainly focuses on the application of distributed target tracking and state detection in wireless sensor networks, and studies how to minimize the network traffic while ensuring performance, thus reducing the energy consumption of sensor nodes and improving network life. wherein the target tracking is a typical estimation problem, the position and the speed of each moment of the target are estimated, and the state detection is used for determining which state of the current environment is a detection problem. When target tracking is performed with a wireless sensor network, the information contained in the data of each sensor node is different. Some of the nodes' data may contain little information, and there is little help to improve tracking performance, so it is necessary to plan the node set and participation mode involved in the target tracking during the tracking process. A target tracking algorithm based on leadership nodes and a fully distributed target tracking algorithm are presented in this paper. In the target tracking algorithm based on the leader node, the data collection process and the communication overhead generated by the leader node migration are comprehensively considered, and the node planning is performed according to the error matrix in the tracking process so as to maximize the performance of the target tracking. By using Gauss-Seidel iteration and convex relaxation in the solution, the complex optimization problem can be solved quickly. Simulation results show that the algorithm can achieve better tracking performance under the same communication energy constraints. The fully distributed target tracking algorithm usually transmits local information throughout the whole network by using a consistency algorithm (Consensus Al0.8m) in each tracking process, so that each node obtains global or near global tracking performance. As previously mentioned, some sensor nodes have little help to improve tracking performance, and participating in a consistent iteration will only waste energy and reduce the convergence speed of the coherence algorithm. In this paper, the node planning strategy is introduced into the distributed target tracking algorithm, and only the nodes with high information content are selected to participate in the consistency calculation at a time, so that the communication overhead of the network is reduced while keeping track performance as much as possible. In the distributed state detection problem, each node not only relies on its own observation data, but also detects the distributed state detection. however, direct transmission of observed data traffic between neighbors is large. This paper applies the social learning mechanism to the distributed state detection, and proposes a distributed state detection algorithm based on private data and neighbor decision, and analyzes the convergence of the algorithm. Compared with the direct exchange observation data or the likelihood ratio and the like, the network communication quantity can be significantly reduced compared with the direct exchange observation data or the likelihood ratio and the like, and when the state space of the environment is binary, the information exchange between the neighbors is small to only 1bit. Simulation results show that under the same communication overhead, the algorithm can converge faster to the real state of the environment.
【學(xué)位授予單位】：復(fù)旦大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.7

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