本文選題：無(wú)線傳感器網(wǎng)絡(luò) + 卡爾曼濾波��； 參考：《浙江大學(xué)》2016年博士論文

【摘要】：無(wú)線傳感器網(wǎng)絡(luò)是21世紀(jì)以來(lái)最重要的技術(shù)之一,其廣泛應(yīng)用極大提高了人類認(rèn)知和改造世界的能力。無(wú)線傳感器網(wǎng)絡(luò)有著分布式的特性,傳感器節(jié)點(diǎn)是網(wǎng)絡(luò)的基本單元,其計(jì)算、存儲(chǔ)和通信能力極其有限。如何合理調(diào)度節(jié)點(diǎn)資源,實(shí)現(xiàn)優(yōu)化配置,構(gòu)成功能強(qiáng)大的網(wǎng)絡(luò),是傳感器管理的核心。本文將結(jié)合遠(yuǎn)程網(wǎng)絡(luò)監(jiān)測(cè)系統(tǒng),研究傳感器的協(xié)同狀態(tài)估計(jì)和傳輸時(shí)序規(guī)劃,討論如何利用傳感器的局部通信,優(yōu)化測(cè)量信息的融合,實(shí)現(xiàn)本地狀態(tài)估計(jì);以及如何規(guī)劃數(shù)據(jù)傳輸時(shí)序,確保遠(yuǎn)程狀態(tài)估計(jì)滿足給定精度指標(biāo)。遠(yuǎn)程監(jiān)測(cè)系統(tǒng)中存在諸多監(jiān)測(cè)進(jìn)程,集中式的處理方式涉及海量數(shù)據(jù)的傳輸,影響了系統(tǒng)的整體效率。本文研究相對(duì)感測(cè)網(wǎng)絡(luò)的分布式估計(jì)問題,以去除冗余信息、減少數(shù)據(jù)傳輸。網(wǎng)絡(luò)中所有節(jié)點(diǎn)都能測(cè)量其自身與鄰居所對(duì)應(yīng)的進(jìn)程之間的相對(duì)狀態(tài),錨定節(jié)點(diǎn)能額外測(cè)量進(jìn)程的絕對(duì)狀態(tài)。本文利用雙向圖對(duì)網(wǎng)絡(luò)拓?fù)溥M(jìn)行建模,通過(guò)構(gòu)造最優(yōu)估計(jì)器以論述連通圖是全局狀態(tài)估計(jì)的基礎(chǔ)條件,設(shè)計(jì)了兩種類似卡爾曼濾波器的次優(yōu)估計(jì)器和一種平均一致的次優(yōu)估計(jì)器,實(shí)現(xiàn)了進(jìn)程全局狀態(tài)的傳感器本地估計(jì)。無(wú)線傳感器網(wǎng)絡(luò)的通信能力有限,為了避免通信沖突并降低系統(tǒng)能耗,本文研究了并行卡爾曼濾波器的時(shí)序規(guī)劃問題。在單信道通信限制條件下,規(guī)劃傳感器的傳輸序列,使所有進(jìn)程在遠(yuǎn)程中心的狀態(tài)估計(jì)時(shí)刻滿足給定精度。系統(tǒng)中,進(jìn)程、傳感器和遠(yuǎn)程估計(jì)器是一一對(duì)應(yīng)的。本文定義并計(jì)算了實(shí)時(shí)傳輸截止期限,將基于協(xié)方差的時(shí)序規(guī)劃問題變?yōu)槿蝿?wù)限期安排問題;在此基礎(chǔ)上,設(shè)計(jì)了基于傳輸周期的離線規(guī)劃方法和基于滑動(dòng)窗口的在線調(diào)整方法,以適應(yīng)傳輸失敗和進(jìn)程估計(jì)閾值的變化。系統(tǒng)中存在大量進(jìn)程時(shí),單信道通信不能保證數(shù)據(jù)及時(shí)傳輸。針對(duì)這一問題,本文研究了自適應(yīng)的多信道傳輸規(guī)劃方法,在保障估計(jì)精度的同時(shí)減少信道占用數(shù)量。本文論證了一種準(zhǔn)確計(jì)算進(jìn)程傳輸周期的夾擠算法;為適應(yīng)多信道傳輸,改進(jìn)了一種基于緩存的離線規(guī)劃算法,并設(shè)計(jì)了相應(yīng)的在線調(diào)整算法;多信道算法簡(jiǎn)化了調(diào)整過(guò)程中的計(jì)算,提高了空閑信道利用效率,縮短了系統(tǒng)變動(dòng)后的調(diào)整時(shí)間,更適應(yīng)分布式網(wǎng)絡(luò)。對(duì)于提出的各種算法設(shè)計(jì),本文在相應(yīng)章節(jié)中給出了具體的理論分析,并通過(guò)仿真實(shí)驗(yàn)進(jìn)行了驗(yàn)證。
[Abstract]:Wireless sensor network (WSN) is one of the most important technologies in the 21st century. Its wide application has greatly improved the ability of human beings to recognize and transform the world. Wireless sensor networks have distributed characteristics. Sensor nodes are the basic unit of the network, and their computing, storage and communication capabilities are extremely limited. It is the core of sensor management that how to reasonably schedule node resources, realize optimal configuration and form a powerful network. In this paper, based on the remote network monitoring system, the cooperative state estimation and transmission timing planning of sensors are studied, and how to use the local communication of sensors to optimize the fusion of measurement information to realize local state estimation is discussed. And how to plan the time series of data transmission to ensure that the remote state estimation meets the given precision index. There are many monitoring processes in the remote monitoring system. The centralized processing involves the transmission of massive data, which affects the overall efficiency of the system. In this paper, the distributed estimation problem of relative sensing network is studied to remove redundant information and reduce data transmission. All nodes in the network can measure the relative states between themselves and their neighbors, and anchoring nodes can measure the absolute states of the processes. In this paper, a bidirectional graph is used to model the network topology, and an optimal estimator is constructed to show that the connected graph is the basic condition of global state estimation. Two suboptimal estimators similar to Kalman filter and a suboptimal estimator with uniform average are designed to realize the local sensor estimation of the global state of the process. Wireless sensor networks have limited communication capability. In order to avoid communication conflicts and reduce system energy consumption, the parallel Kalman filter timing planning problem is studied in this paper. Under the condition of single channel communication limitation, the transmission sequence of sensors is planned so that all processes meet the given precision at the state estimation of remote center. In the system, processes, sensors and remote estimators are one-to-one correspondence. In this paper, the deadline for real-time transmission is defined and calculated, which turns the covariance-based time series programming problem into a task deadline scheduling problem. The off-line planning method based on transmission period and the on-line adjustment method based on sliding window are designed to adapt to the change of transmission failure and process estimation threshold. When there are a large number of processes in the system, single channel communication can not guarantee the timely transmission of data. To solve this problem, an adaptive multi-channel transmission planning method is proposed in this paper, which not only guarantees the estimation accuracy but also reduces the amount of channel occupancy. In this paper, an algorithm for accurately calculating the process transmission cycle is demonstrated, and an off-line planning algorithm based on cache is improved to adapt to multi-channel transmission, and the corresponding on-line adjustment algorithm is designed. Multi-channel algorithm simplifies the calculation in the adjustment process, improves the efficiency of idle channel utilization, shortens the adjusting time after system change, and is more suitable for distributed networks. For the various algorithms proposed in this paper, the specific theoretical analysis is given in the corresponding chapters, and verified by simulation experiments.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP212.9;TN929.5

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 肖力;孫志剛;胡曉婭;陳綿云;;基于傳感器網(wǎng)絡(luò)的遠(yuǎn)程狀態(tài)估計(jì)[J];控制理論與應(yīng)用;2009年07期

2 劉先省,申石磊,潘泉;傳感器管理及方法綜述[J];電子學(xué)報(bào);2002年03期

，

本文編號(hào)：1851704