本文選題：水下無(wú)線傳感器網(wǎng)絡(luò)　切入點(diǎn)：無(wú)序量測(cè)　出處：《天津工業(yè)大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：在水下隨機(jī)或有規(guī)律部署大量低功耗、具有一定的通信能力的傳感器節(jié)點(diǎn),采集水下信息,利用節(jié)點(diǎn)的自組織能力,形成水下無(wú)線傳感器網(wǎng)絡(luò)(Underwater Wireless Sensor Network, UWSN)。UWSN為水下環(huán)境監(jiān)測(cè)提供新的手段,但UWSN通信采用聲波通信,不同于傳統(tǒng)的無(wú)線傳感器網(wǎng)絡(luò)(WSN),帶來(lái)了UWSN的定位技術(shù)、網(wǎng)絡(luò)拓?fù)洹⒙酚蓞f(xié)議、水聲通信和數(shù)據(jù)融合等方面的問(wèn)題。UWSN的數(shù)據(jù)融合目的是通過(guò)融合減少數(shù)據(jù)的傳輸量,降低節(jié)點(diǎn)因數(shù)據(jù)傳輸帶來(lái)的能耗,對(duì)延長(zhǎng)UWSN的生命周期具有重要的現(xiàn)實(shí)意義。水聲信道具有傳播延遲高且動(dòng)態(tài)變化、通信帶寬有限、多徑效應(yīng)嚴(yán)重等特點(diǎn),受自身物理?xiàng)l件的制約,UWSN中易出現(xiàn)節(jié)點(diǎn)先發(fā)的信息后到融合中心,后發(fā)的信息先到融合中心的現(xiàn)象,即無(wú)序量測(cè)(Out-of-Sequence Measurement, OOSM)現(xiàn)象。因此,在UWSN中,針對(duì)OOSM現(xiàn)象,如何融合處理帶OOSM數(shù)據(jù),直接關(guān)系到融合結(jié)果的可信度,也促使數(shù)據(jù)融合理論的完善。本文從單傳感器和多傳感器方面研究UWSN網(wǎng)中的OOSM數(shù)據(jù)融合問(wèn)題,主要工作如下：(1)使用OPNET對(duì)水聲信道進(jìn)行仿真,建立水聲通信網(wǎng)絡(luò)模型,分析網(wǎng)絡(luò)延遲,觀測(cè)無(wú)序量測(cè)現(xiàn)象。(2)對(duì)UWSN環(huán)境下的單傳感器單步延遲無(wú)序量測(cè)融合算法進(jìn)行了研究,提出基于后向預(yù)測(cè)的無(wú)序量測(cè)融合算法。在過(guò)程噪聲直接離散化模型條件下,該算法能夠處理量測(cè)噪聲與同一時(shí)刻過(guò)程噪聲相關(guān)的單傳感器單步延遲無(wú)序量測(cè)數(shù)據(jù)融合問(wèn)題。該算法可以保證實(shí)時(shí)性,而且濾波精度高于A1算法,仿真結(jié)果驗(yàn)證了算法的有效性。(3)針對(duì)UWSN環(huán)境下的多傳感器單步延遲無(wú)序量測(cè)問(wèn)題,提出一種基于等價(jià)量測(cè)的單步延遲無(wú)序量測(cè)融合算法。該算法采用分布式融合結(jié)構(gòu),利用一步預(yù)測(cè)的等價(jià)量測(cè)處理延遲量測(cè),把無(wú)序量測(cè)信息融合問(wèn)題轉(zhuǎn)化成一種順序量測(cè)信息融合問(wèn)題,采用噪聲相關(guān)的Kalman濾波算法獲得局部估計(jì),最后使用矩陣加權(quán)算法得到全局估計(jì)。該算法的濾波精度與直接更新法相比有所降低,但計(jì)算量減少。
[Abstract]:A large number of sensor nodes with low power consumption and certain communication capability are deployed randomly or regularly under water to collect underwater information and make use of the self-organizing capability of the nodes. The formation of underwater Wireless Sensor Network, UWSN).UWSN provides a new means for underwater environment monitoring, but the UWSN communication uses acoustic communication, which is different from the traditional wireless sensor network (WSNN), and brings the location technology, network topology, routing protocol of UWSN. The purpose of UWSN data fusion is to reduce the amount of data transmission and reduce the energy consumption caused by data transmission. The underwater acoustic channel has the characteristics of high propagation delay, dynamic variation, limited communication bandwidth and serious multipath effect. Under the restriction of physical conditions, the phenomenon of node first sending information to fusion center and later information to fusion center is easy to occur in UWSN, that is, out-of-order measurement Out-of-Sequence measure (OOSMN) phenomenon. Therefore, in UWSN, the phenomenon of OOSM is discussed. How to fuse and process the data with OOSM is directly related to the credibility of the fusion results and the perfection of the theory of data fusion. In this paper, the problem of OOSM data fusion in UWSN nets is studied from the single sensor and multi-sensor aspects. The main work is as follows: (1) the underwater acoustic channel is simulated with OPNET, the network model of underwater acoustic communication is established, the network delay is analyzed, and the disordered measurement phenomenon is observed. An unordered measurement fusion algorithm based on backward prediction is proposed. Under the condition of direct discretization of process noise, The algorithm can deal with the data fusion problem of single sensor delay unordered measurement which is related to the measurement noise and the process noise at the same time. The algorithm can ensure the real-time performance and the filtering accuracy is higher than that of A1 algorithm. Simulation results show that the algorithm is effective. Aiming at the multi-sensor single-step delay disorder measurement problem in UWSN environment, a one-step delay disorder measurement fusion algorithm based on equivalent measurement is proposed. The algorithm adopts a distributed fusion structure. By using one-step prediction equivalent measure processing delay measurement, the unordered measurement information fusion problem is transformed into a sequential measurement information fusion problem, and the local estimation is obtained by using the noise-dependent Kalman filtering algorithm. Finally, the global estimation is obtained by using the matrix weighting algorithm. The filtering accuracy of the algorithm is lower than that of the direct updating method, but the computational complexity is reduced.
【學(xué)位授予單位】：天津工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP212.9;TN929.3

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 G.O.斯屈克爾;磁調(diào)制光電倍加管的超光度計(jì)[J];物理學(xué)報(bào);1958年01期

2 羅伊L·斯雷特 ,夢(mèng)鄉(xiāng);隨機(jī)多假設(shè)跟蹤的最大似然法[J];情報(bào)指揮控制系統(tǒng)與仿真技術(shù);1998年09期

3 閔泳川;激光功率測(cè)量及其定標(biāo)[J];電子器件;1978年04期

4 安杰;;安捷倫電子量測(cè)論壇2011年6月底舉辦[J];郵電設(shè)計(jì)技術(shù);2011年06期

5 喻光明 ,劉美文;膜片法在圖形面積量測(cè)中若干問(wèn)題的探討——Ⅱ、膜片的選擇等問(wèn)題[J];咸寧師專學(xué)報(bào);1988年S1期

6 閆小喜;韓崇昭;李威;連峰;;拓展目標(biāo)量測(cè)集合分割算法[J];西安交通大學(xué)學(xué)報(bào);2014年09期

7 楊永健;陳義平;張書濱;殷杰波;;量測(cè)誤差的時(shí)間序列分析方法[J];指揮控制與仿真;2008年03期

8 田世明;徐仁武;;高級(jí)量測(cè)體系關(guān)鍵技術(shù)研究[J];電信科學(xué);2010年S3期

9 侯書東;王志紅;尹現(xiàn)軍;;一種精確協(xié)方差估計(jì)的轉(zhuǎn)換量測(cè)卡爾曼濾波方法[J];艦船電子對(duì)抗;2014年03期

10 李光磊;劉佳;;一種量測(cè)型組合導(dǎo)航系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[J];艦船電子工程;2011年03期

相關(guān)會(huì)議論文 前10條

1 謝佩芬;;MSA}q，

本文編號(hào)：1562807