發(fā)布時間：2018-04-28 12:41

  本文選題：無線傳感器網(wǎng)絡 + RSSI　； 參考：《安徽大學》2017年碩士論文

【摘要】：隨著無線傳感器網(wǎng)絡技術的不斷發(fā)展,無線傳感器網(wǎng)絡越來越多地應用于環(huán)境感知、目標監(jiān)測等方面。已有的無線傳感器定位技術多為有源定位,定位目標需要綁定有源器件或電子標簽,在條件受限的環(huán)境中適用性不高。近年來利用無線傳感器網(wǎng)絡節(jié)點的接收信號強度指示RSSI(Received Signal Strength Indication)進行無源感知定位(Device-free Localization)的概念被提出:通過目標對無線信號的吸收、反射、折射作用而造成的鏈路RSSI變化,實現(xiàn)對感知目標位置的估計。無源感知定位使目標脫離了有源電子器件的限制,具有更強的靈活性與適用性,其中定位算法成為無源感知定位研究的重點。本文首先對無線傳感器網(wǎng)絡與無源感知定位技術進行了概述,對各類定位算法模型進行了分類與比較,重點研究了基于RSSI的無源感知定位算法,將網(wǎng)格法(Sensor Grid Array)與橢圓測量模型(Ellipse Measurement Model)結合,提出了基于網(wǎng)格法與橢圓測量模型的無源感知定位算法。算法首先根據(jù)網(wǎng)格法定位原理,利用傳感器節(jié)點鏈路對感知區(qū)域進行劃分,再通過比較鏈路RSSI的相對變化對鏈路進行篩選并結合橢圓測量模型繪制圓環(huán)線,依據(jù)橢圓交點確定感知目標位置。在確定算法的基本流程后,需對所使用的橢圓模型參數(shù)進行調(diào)整以適應算法的整體過程。因此本文在以CC2530芯片為核心的ZigBee無線傳感器網(wǎng)絡實驗平臺上進行定位實驗,通過對定位誤差數(shù)據(jù)的收集與分析,實現(xiàn)了模型參數(shù)的反向推導,確定了算法的最終形式。算法確定后,在原實驗平臺的基礎上分別改變傳感器節(jié)點的數(shù)目與感知區(qū)域面積,重新進行無源感知定位實驗,完成實驗數(shù)據(jù)收集并配合MATLAB進行算法仿真與模擬定位,驗證了算法的有效性。通過定位誤差分析與算法比較可以發(fā)現(xiàn):基于網(wǎng)格法與橢圓測量模型的無源感知定位算法相較于傳統(tǒng)的網(wǎng)格法能夠以較低的傳感器節(jié)點密度獲得相近的定位精度,但會使定位產(chǎn)生邊緣效應;相較于掃描成像法,基于網(wǎng)格法與橢圓測量模型的無源感知定位算法能夠以更低的節(jié)點硬件成本獲得更高的定位精度。
[Abstract]:With the development of wireless sensor network technology, wireless sensor network is more and more used in environmental perception, target monitoring and so on. Most of the existing wireless sensor localization techniques are active localization, the target location needs to bind active devices or electronic tags, so it is not suitable for the environment with limited conditions. In recent years, using the received signal strength of wireless sensor network nodes to indicate the RSSI(Received Signal Strength Indication) for passive sensing positioning has been proposed: the link RSSI changes caused by the target's absorption, reflection and refraction of the wireless signal. The estimation of target location is realized. Passive sensing localization makes the target get rid of the limitation of active electronic devices, so it has more flexibility and applicability, among which the localization algorithm becomes the focus of passive sensing localization research. Firstly, this paper summarizes the wireless sensor network and passive sensing localization technology, classifies and compares all kinds of localization algorithm models, and focuses on the passive sensing location algorithm based on RSSI. A passive sensing location algorithm based on mesh method and elliptic measurement model is proposed by combining the mesh method and Ellipse Measurement Model. Firstly, according to the principle of grid positioning, the sensor node link is used to divide the sensing region, then the link is screened by comparing the relative changes of link RSSI and the circle line is drawn by combining with the elliptical measurement model. The position of the perceived target is determined according to the intersection of the ellipse. After determining the basic flow of the algorithm, the parameters of the elliptic model should be adjusted to fit the whole process of the algorithm. Therefore, this paper carries on the localization experiment on the ZigBee wireless sensor network experiment platform with CC2530 chip as the core. Through collecting and analyzing the positioning error data, the inverse derivation of the model parameters is realized, and the final form of the algorithm is determined. After the algorithm is determined, on the basis of the original experimental platform, the number of sensor nodes and the area of sensing area are changed, and the passive sensing localization experiment is carried out again, and the experimental data are collected and the algorithm is simulated and simulated with MATLAB. The validity of the algorithm is verified. Through the analysis of the location error and the comparison of the algorithm, it can be found that compared with the traditional mesh method, the passive sensing localization algorithm based on mesh method and elliptical measurement model can obtain similar positioning accuracy with lower sensor node density. Compared with the scanning imaging method, the passive sensing localization algorithm based on mesh method and elliptical measurement model can achieve higher localization accuracy with lower node hardware cost.
【學位授予單位】：安徽大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP212.9;TN929.5

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本文編號：1815311