發(fā)布時(shí)間：2018-06-13 11:06

  本文選題：移動(dòng)無(wú)線傳感器網(wǎng)絡(luò) + 中垂線法　； 參考：《中國(guó)礦業(yè)大學(xué)》2017年碩士論文

【摘要】：移動(dòng)節(jié)點(diǎn)定位問(wèn)題是無(wú)線傳感器網(wǎng)絡(luò)的研究熱點(diǎn)之一,它利用少量位置已知節(jié)點(diǎn),按照某種機(jī)制對(duì)未知節(jié)點(diǎn)進(jìn)行定位。現(xiàn)有算法存在僅適用于靜止節(jié)點(diǎn)、測(cè)距方法受環(huán)境影響大、定位精度低等問(wèn)題,隨著無(wú)線傳感器網(wǎng)絡(luò)技術(shù)的進(jìn)步和完善,越來(lái)越多場(chǎng)合需要定位技術(shù)的支撐,因此對(duì)移動(dòng)無(wú)線傳感器網(wǎng)絡(luò)技術(shù)的研究有重要理論意義和應(yīng)用價(jià)值�，F(xiàn)有移動(dòng)節(jié)點(diǎn)定位算法充分利用節(jié)點(diǎn)的移動(dòng)性減小定位復(fù)雜度和硬件代價(jià),可是忽略了一些潛在定位信息,濾波條件少,導(dǎo)致定位時(shí)間長(zhǎng)、定位精度低。為了解決這個(gè)問(wèn)題,本文提出一種基于信號(hào)強(qiáng)度差異的移動(dòng)節(jié)點(diǎn)定位算法(NMCL),充分提取潛在的可能位置信息,利用中垂線法和近似三角形內(nèi)點(diǎn)測(cè)法對(duì)區(qū)域進(jìn)行劃分,縮小未知節(jié)點(diǎn)可能存在的位置區(qū)域,從而提高定位精度。此外,針對(duì)NMCL算法中始終以未知節(jié)點(diǎn)移動(dòng)最大速度作為固定的采樣半徑,導(dǎo)致預(yù)測(cè)階段采樣面積過(guò)大,定位精度低的問(wèn)題,本文還提出一種基于信號(hào)強(qiáng)度差異的速度自適應(yīng)改進(jìn)算法(INMCL),根據(jù)這一時(shí)刻和上一時(shí)刻未知節(jié)點(diǎn)接收到的信號(hào)強(qiáng)度差異,計(jì)算未知節(jié)點(diǎn)單位時(shí)間內(nèi)移動(dòng)的特征距離,再將特征距離轉(zhuǎn)化為物理距離,作為預(yù)測(cè)時(shí)采樣區(qū)域的半徑,從而根據(jù)節(jié)點(diǎn)移動(dòng)速度實(shí)時(shí)變化采樣面積,提高定位精度。大量的仿真模擬表明,NMCL算法和INMCL算法較現(xiàn)有移動(dòng)算法在定位速度較大、錨節(jié)點(diǎn)密度較低、通信不規(guī)則度強(qiáng)等情況下,定位精度有很大的提高。
[Abstract]:Mobile node localization is one of the research hotspots in wireless sensor networks. It uses a small number of known nodes to locate unknown nodes according to some mechanism. The existing algorithms are only applicable to stationary nodes, ranging methods are greatly affected by the environment, low positioning accuracy and other problems. With the progress and improvement of wireless sensor network technology, more and more occasions need the support of location technology. Therefore, the research of mobile wireless sensor network technology has important theoretical significance and application value. The existing mobile node localization algorithms make full use of the mobility of the nodes to reduce the localization complexity and hardware cost, but ignore some potential location information, so the filtering conditions are less, resulting in long localization time and low positioning accuracy. In order to solve this problem, a mobile node location algorithm based on signal intensity difference is proposed in this paper, which can fully extract the potential position information, and divide the region by using the method of mid-vertical line and the method of approximate triangle point measurement. The location of unknown nodes may be reduced to improve the positioning accuracy. In addition, in the NMCL algorithm, the maximum velocity of unknown nodes is always taken as the fixed sampling radius, which leads to the problem that the sampling area is too large and the positioning accuracy is low in the prediction stage. This paper also presents an improved speed adaptive algorithm based on signal strength difference. According to the difference of signal strength between the unknown nodes at this time and the previous moment, the characteristic distance of the unknown node moving in unit time is calculated. Then the feature distance is transformed into physical distance, which is the radius of the sampling area during prediction, and the sampling area is changed in real time according to the moving speed of the node, and the positioning accuracy is improved. A large number of simulations show that the localization accuracy of NMCL algorithm and INMCL algorithm is much higher than that of the existing algorithms under the conditions of higher localization speed, lower anchor node density and stronger communication irregularity.
【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9;TN929.5

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本文編號(hào)：2013818