本文關(guān)鍵詞： 無線傳感器網(wǎng)絡(luò) RSSI 卡爾曼濾波 線性回歸 移動節(jié)點　出處：《南京航空航天大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：無線傳感器網(wǎng)絡(luò)(WSN，Wireless Sensor Network)作為獲取信息的重要手段，在軍事戰(zhàn)場、環(huán)境監(jiān)測、醫(yī)療護(hù)理等諸多領(lǐng)域有廣泛的應(yīng)用。而對于諸多應(yīng)用如目標(biāo)跟蹤、森林火災(zāi)監(jiān)控、遠(yuǎn)程數(shù)據(jù)采集等都需要確定的位置信息，因此節(jié)點定位技術(shù)對WSN的應(yīng)用至關(guān)重要，是WSN核心技術(shù)之一。 本文主要研究了復(fù)雜條件下的節(jié)點定位技術(shù)。針對RSSI值常常受到各種復(fù)雜條件的干擾，提出了基于卡爾曼濾波的RSSI值篩選策略。另外一方面，在RSSI值與實際距離的信號衰減模型中，提出了信號衰減方程與線性回歸理論結(jié)合的方法獲取準(zhǔn)確的環(huán)境參數(shù)，修正RSSI測距模型。實驗證明該方法能夠有效的抑制干擾對測距結(jié)果的影響。 一些定位場合中，錨節(jié)點的個數(shù)超過三個，其性能會有優(yōu)劣之分，應(yīng)該盡量選取性能較好的錨節(jié)點進(jìn)行定位，，否則將性能較差的錨節(jié)點加入定位會造成定位誤差增大。因此本文提出了一種基于線性回歸分析的定位策略。用相關(guān)系數(shù)和剩余標(biāo)準(zhǔn)差對錨節(jié)點的測距模型進(jìn)行性能評估，制定了一套適合實際情況的定位策略。實驗表明，采用修正的測距模型和新的定位策略，使得節(jié)點定位精度明顯提高。 為了解決錨節(jié)點稀疏的條件下，傳統(tǒng)的靜態(tài)節(jié)點定位方法往往無法實現(xiàn)移動節(jié)點定位的問題，提出了一種基于RSSI測距和蒙特卡洛預(yù)測相結(jié)合的移動節(jié)點定位算法。當(dāng)移動節(jié)點進(jìn)入錨節(jié)點稀疏區(qū)域時，通過RSSI測距和最小二乘曲線擬合預(yù)測當(dāng)前時刻各自的采樣區(qū)域，將最終的采樣區(qū)域縮小在它們的交集以內(nèi)，縮減采樣區(qū)域的同時提升了樣本采樣成功率和定位的精度。最后利用RSSI線性回歸分析技術(shù)和基于預(yù)測的移動節(jié)點定位方法相結(jié)合，實現(xiàn)了錨節(jié)點不同密度情況下的移動節(jié)點定位。實驗表明，該定位方法效果良好。
[Abstract]:Wireless sensor network (WSNN Wireless Sensor Network) as an important means to obtain information, in the military battlefield, environmental monitoring. Medical care and many other fields have a wide range of applications, but for many applications such as target tracking, forest fire monitoring, remote data collection and other needs to determine the location information. Therefore, node location technology is very important to the application of WSN, and is one of the core technologies of WSN. In this paper, we mainly study the node location technology under complex conditions. Aiming at the RSSI value is often disturbed by various complex conditions, a RSSI value filtering strategy based on Kalman filter is proposed. In the signal attenuation model between the RSSI value and the actual distance, the method of combining the signal attenuation equation with the linear regression theory is proposed to obtain the accurate environmental parameters. The RSSI rangefinder model is modified. Experiments show that the proposed method can effectively suppress the influence of interference on ranging results. In some positioning situations, the number of anchor nodes is more than three, its performance will have advantages and disadvantages, we should try to select better anchor nodes to locate. Otherwise, the location error will increase if the anchor node with poor performance is added. Therefore, a localization strategy based on linear regression analysis is proposed in this paper. Correlation coefficient and residual standard deviation are used to carry out the location model of anchor node. Performance evaluation. A set of localization strategies suitable for the actual situation is established. The experimental results show that the modified ranging model and the new location strategy can obviously improve the accuracy of node localization. In order to solve the problem that the traditional static node localization method can not realize the mobile node location under the condition of sparse anchor nodes. A mobile node location algorithm based on the combination of RSSI ranging and Monte Carlo prediction is proposed, when the mobile node enters the sparse area of the anchor node. The RSSI ranging and least square curve fitting are used to predict the respective sampling regions at the current time, and the final sampling regions are reduced to their intersection. At the same time, the sample sampling success rate and localization accuracy are improved by reducing the sampling area. Finally, the RSSI linear regression analysis technique and the mobile node location method based on prediction are combined. The location of mobile nodes with different density of anchor nodes is realized, and the experimental results show that the proposed method is effective.
【學(xué)位授予單位】：南京航空航天大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP212.9;TN929.5

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