【摘要】：隨著計算機(jī)技術(shù)、通信技術(shù)以及嵌入式系統(tǒng)的迅速發(fā)展,無線傳感器網(wǎng)絡(luò)(WSN)已成為一個重要的研究領(lǐng)域。WSN是把大量的傳感器節(jié)點(diǎn)隨機(jī)地播撒在監(jiān)測區(qū)域內(nèi),通過自動組網(wǎng)的方式形成智能網(wǎng)絡(luò)系統(tǒng)來對目標(biāo)進(jìn)行監(jiān)測,之后對監(jiān)測的數(shù)據(jù)進(jìn)行處理并傳送到管理用戶。節(jié)點(diǎn)定位技術(shù)是WSN進(jìn)行監(jiān)控、預(yù)報和識別等應(yīng)用技術(shù)的基礎(chǔ),在整個網(wǎng)絡(luò)中通常選取少部分節(jié)點(diǎn)帶有自定位的GPS模塊并將它們視為錨節(jié)點(diǎn),而待測節(jié)點(diǎn)則采用自組能力強(qiáng)、連通性好、定位精度高的定位算法來實(shí)現(xiàn)節(jié)點(diǎn)定位。本文主要研究了依賴測距的定位算法和非依賴測距的定位算法。其中依賴測距的定位算法主要是測量各節(jié)點(diǎn)間的角度或距離等信息,定位精度相對來說較高,而且能夠滿足很多應(yīng)用的需求。因此對基于接收信號強(qiáng)度指示(RSSI)的無線傳感器網(wǎng)絡(luò)定位算法進(jìn)行深入研究,由于該定位算法在節(jié)點(diǎn)的測距和定位過程中都存在較大誤差,因此本文對該算法的誤差進(jìn)行了改進(jìn)。改進(jìn)的節(jié)點(diǎn)定位算法首先是通過建立Kalman濾波器模型并利用其對通信節(jié)點(diǎn)的RSSI信號值進(jìn)行平滑處理,使待測節(jié)點(diǎn)接收到的信號強(qiáng)度值更趨近于真實(shí)值;然后通過選取待測節(jié)點(diǎn)和與其相鄰的各錨節(jié)點(diǎn)之間距離倒數(shù)的和作為權(quán)值因子,同時利用通信節(jié)點(diǎn)間的測量距離對權(quán)值因子加以修正,再利用二次加權(quán)質(zhì)心定位算法定位待測節(jié)點(diǎn)的位置坐標(biāo);最后再通過定位距待測節(jié)點(diǎn)相近的錨節(jié)點(diǎn)來得出定位均差,對待測節(jié)點(diǎn)的坐標(biāo)進(jìn)行誤差補(bǔ)償。通過仿真實(shí)驗(yàn),本文提出的改進(jìn)定位算法比基于RSSI的加權(quán)質(zhì)心算法提高了5%-6%的定位精度。
[Abstract]:With the rapid development of computer technology, communication technology and embedded system, wireless sensor network (WSN) has become an important research field. WSN is to spread a large number of sensor nodes randomly in the monitoring area. An intelligent network system is formed by automatic networking to monitor the target, and then the monitored data are processed and transmitted to the management user. Node location technology is the basis of WSN monitoring, prediction and identification technology. In the whole network, a few nodes with self-positioning GPS modules are usually selected and regarded as anchor nodes, while the nodes under test have strong self-organization ability. The location algorithm with good connectivity and high positioning accuracy is used to realize node localization. In this paper, we mainly study the location algorithm based on distance measurement and the location algorithm of non-dependent ranging. The localization algorithm based on ranging is mainly used to measure the angle or distance between the nodes. The location accuracy is relatively high and can meet the needs of many applications. Therefore, the location algorithm of wireless sensor networks based on received signal intensity indication (RSSI) is deeply studied. Because of the large errors in the location and location process of nodes, the error of the algorithm is improved in this paper. The improved node localization algorithm firstly establishes the Kalman filter model and uses it to smooth the RSSI signal value of the communication node so that the signal intensity value of the node under test is closer to the real value. Then the reciprocal sum of the distance between the nodes to be tested and the adjacent anchor nodes is selected as the weight factor. At the same time, the weight factor is modified by the measurement distance between the communication nodes. The second weighted centroid positioning algorithm is used to locate the coordinates of the nodes to be tested. Finally, the location mean difference is obtained by locating the anchor nodes which are close to the measured nodes, and the errors of the measured nodes are compensated. Through simulation experiments, the improved localization algorithm proposed in this paper improves the positioning accuracy by 5- 6% compared with the weighted centroid algorithm based on RSSI.
【學(xué)位授予單位】：沈陽航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9;TN929.5

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