本文選題：WSN + 定位算法��； 參考：《重慶大學(xué)》2014年碩士論文

【摘要】：為了解決復(fù)雜環(huán)境下無(wú)線傳感器網(wǎng)絡(luò)（Wireless Sensor Network, WSN）定位技術(shù)定位精度和計(jì)算量難以兼顧，，本文研究了復(fù)雜環(huán)境下的定位算法。文中分析了多種典型定位算法在復(fù)雜環(huán)境下應(yīng)用的優(yōu)缺點(diǎn)，研究了基于接收信號(hào)強(qiáng)度（Received Signal Strength Indicator, RSSI）距離測(cè)量模型的位置計(jì)算方法。基于測(cè)距定位算法，提出了改進(jìn)的極大似然估計(jì)求解方法和概率質(zhì)心兩類(lèi)定位算法。課題完成了以下研究工作，并取得了一定的研究成果。 首先，基于模擬仿真實(shí)驗(yàn)平臺(tái)，分析了幾類(lèi)經(jīng)典定位算法在復(fù)雜環(huán)境下的定位性能，找出了影響定位算法定位性能的原因，明確了復(fù)雜環(huán)境下定位算法的改進(jìn)方向。 其次，通過(guò)對(duì)復(fù)雜環(huán)境下的極大似然估計(jì)定位算法研究，發(fā)現(xiàn)原求解方法因計(jì)算量大限制了該算法在復(fù)雜環(huán)境的應(yīng)用，從而提出了一種新的極大似然估計(jì)極值點(diǎn)求解方法，該方法可直接求取未知節(jié)點(diǎn)估計(jì)位置，即極大似然估計(jì)的極值點(diǎn)。仿真驗(yàn)證了該定位算法的有效性，在復(fù)雜環(huán)境下獲得的定位性能測(cè)試曲線顯示，該算法不僅可降低計(jì)算量，而且具有很強(qiáng)的魯棒性優(yōu)勢(shì)。 最后，對(duì)三邊質(zhì)心定位算法和極大似然估計(jì)定位算法進(jìn)行了比較研究，提出了概率質(zhì)心定位算法。該算法以重疊區(qū)域概率密度函數(shù)作為其密度函數(shù)，即以概率質(zhì)心坐標(biāo)表示未知節(jié)點(diǎn)位置，改善了原三邊質(zhì)心定位算法等同考慮重疊區(qū)域的不足，提高了復(fù)雜環(huán)境下的定位精度。在復(fù)雜環(huán)境下概率質(zhì)心定位算法獲得的仿真定位曲線顯示，該算法既繼承了三邊質(zhì)心定位計(jì)算量低，也確保了較高的定位精度和強(qiáng)魯棒性。 課題研究表明，本文提出的改進(jìn)的極大似然估計(jì)求解方法和概率質(zhì)心定位算法可協(xié)調(diào)定位精度和計(jì)算量?jī)烧叩拿�，并具有�?qiáng)魯棒性的優(yōu)勢(shì)，可適用于復(fù)雜環(huán)境下的無(wú)線傳感器網(wǎng)絡(luò)定位。
[Abstract]:In order to solve the problem of wireless sensor network (WSN) location in complex environment, the localization algorithm in complex environment is studied in this paper. In this paper, the advantages and disadvantages of several typical localization algorithms in complex environments are analyzed, and the position calculation method based on received signal strength (RSSI) distance measurement model is studied. Based on the range location algorithm, an improved maximum likelihood estimation (MLE) algorithm and a probabilistic centroid localization algorithm are proposed. The following research work has been completed, and some research results have been achieved. Firstly, based on the simulation experiment platform, the localization performance of several classical localization algorithms in complex environment is analyzed. The reasons that affect the localization performance of the localization algorithm are found out, and the improvement direction of the localization algorithm in the complex environment is defined. Secondly, the maximum likelihood estimation localization algorithm in the complex environment is studied. It is found that the original solution method limits the application of the algorithm in complex environments because of the large amount of computation. Thus, a new maximum likelihood estimation method is proposed, which can directly calculate the estimated position of unknown nodes. The extremum of maximum likelihood estimation. Simulation results show that the proposed algorithm is effective. The performance test curves obtained in complex environments show that the algorithm can not only reduce the computational complexity, but also has a strong robustness advantage. Three edge centroid localization algorithm and maximum likelihood estimation localization algorithm are compared, and probabilistic centroid localization algorithm is proposed. In this algorithm, the probability density function of overlapping region is used as its density function, that is, the position of unknown node is represented by the coordinate of probabilistic centroid, which improves the localization accuracy in complex environment. The simulation curve obtained by the probabilistic centroid localization algorithm in complex environment shows that the algorithm not only inherits the low computation amount of centroid localization, but also ensures high positioning accuracy and strong robustness. The improved maximum likelihood estimation method and the probabilistic centroid localization algorithm proposed in this paper can reconcile the contradiction between location accuracy and computational complexity, and have the advantage of strong robustness, and can be applied to wireless sensor networks in complex environments.
【學(xué)位授予單位】：重慶大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN929.5;TP212.9

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本文編號(hào)：1996274