本文選題：WLAN/ZigBee + 室內(nèi)定位�。� 參考：《遼寧工業(yè)大學(xué)》2014年碩士論文

【摘要】：如今，室內(nèi)定位技術(shù)在室內(nèi)導(dǎo)航、社交、市場(chǎng)推廣、物流跟蹤等各種位置服務(wù)的應(yīng)用中發(fā)揮著極其重要的作用。由于WLAN網(wǎng)絡(luò)已大量部署且成本低廉，ZigBee憑借功耗小、成本低和可靠性高的優(yōu)點(diǎn)，因此基于泛在網(wǎng)絡(luò)WLAN及ZigBee的室內(nèi)定位已經(jīng)成為定位技術(shù)研究的熱點(diǎn)。為滿足更高的精度要求，室內(nèi)定位技術(shù)的研究趨勢(shì)正由同構(gòu)網(wǎng)絡(luò)向異構(gòu)網(wǎng)絡(luò)協(xié)同定位方向延伸。論文主要做以下三方面的研究。 首先，研究了不同權(quán)值的加權(quán)質(zhì)心定位算法并進(jìn)行對(duì)比分析。針對(duì)加權(quán)質(zhì)心定位算法存在的邊緣區(qū)域定位誤差大的問(wèn)題，提出了一種ZigBee網(wǎng)絡(luò)下基于距離比值的加權(quán)質(zhì)心定位算法。該方法結(jié)合信號(hào)強(qiáng)度距離損耗簡(jiǎn)化模型，采用未知節(jié)點(diǎn)到信標(biāo)節(jié)點(diǎn)的距離比值作為權(quán)值因子，相對(duì)于傳統(tǒng)加權(quán)質(zhì)心算法，可以減小某個(gè)加權(quán)因子值偏大對(duì)定位結(jié)果產(chǎn)生的影響。仿真表明，該方法不僅明顯降低了算法的平均定位誤差，而且對(duì)加權(quán)質(zhì)心算法的邊緣劣勢(shì)有較明顯的改善。 然后，針對(duì)基于粒子群優(yōu)化算法的室內(nèi)定位方法中迭代次數(shù)過(guò)多，容易陷入局部最優(yōu)解的缺點(diǎn)，利用了基于線性時(shí)變慣性權(quán)重的粒子群優(yōu)化算法，根據(jù)迭代次數(shù)調(diào)整慣性權(quán)重的值。針對(duì)測(cè)距誤差越大對(duì)定位誤差影響越大的問(wèn)題，，提出了改進(jìn)的適應(yīng)度函數(shù)，采用殘差加權(quán)的最小二乘法適應(yīng)度函數(shù)代替均方誤差的適應(yīng)度函數(shù)。仿真結(jié)果表明，改進(jìn)的算法不僅能避免陷入局部最優(yōu)值，減少迭代次數(shù)，而且還降低了測(cè)距誤差變化對(duì)定位誤差的影響。 最后，對(duì)異構(gòu)網(wǎng)絡(luò)下的室內(nèi)網(wǎng)絡(luò)定位方法進(jìn)行研究，針對(duì)WLAN和ZigBee網(wǎng)絡(luò)定位各自優(yōu)勢(shì)互補(bǔ)的特性，提出了基于ZigBee加權(quán)質(zhì)心和WLAN粒子群優(yōu)化的異構(gòu)網(wǎng)絡(luò)協(xié)同定位算法。采用加權(quán)質(zhì)心法初始值周邊區(qū)域作為粒子群初始位置區(qū)域，進(jìn)行粒子尋優(yōu)，以縮小粒子群的搜索范圍。仿真結(jié)果顯示，本算法不僅提高了定位精度，縮小了粒子群優(yōu)化算法的初始值區(qū)域，而且減少了迭代次數(shù)，降低了算法的復(fù)雜度。
[Abstract]:Nowadays, indoor positioning technology plays an extremely important role in the application of indoor navigation, social networking, marketing, logistics tracking and other location services. Due to the advantages of low power consumption, low cost and high reliability, WLAN network has been deployed in large numbers. Therefore, indoor positioning based on ubiquitous network WLAN and ZigBee has become a hot spot in localization technology. In order to meet the higher precision requirement, the research trend of indoor positioning technology is extending from isomorphic network to heterogeneous network. The paper mainly does the following three aspects of research. Firstly, the weighted centroid localization algorithm with different weights is studied and compared. A weighted centroid location algorithm based on distance ratio in ZigBee network is proposed to solve the problem of large error of edge location in weighted centroid localization algorithm. Combined with the simplified model of signal intensity distance loss, the ratio of unknown node to beacon node is used as the weight factor. Compared with the traditional weighted centroid algorithm, the influence of the value of a certain weighting factor on the location result can be reduced. Simulation results show that this method not only reduces the average positioning error of the algorithm, but also improves the edge disadvantage of the weighted centroid algorithm. Then, aiming at the shortcomings of the indoor localization method based on particle swarm optimization, which is easy to fall into the local optimal solution, the particle swarm optimization algorithm based on linear time-varying inertia weight is used. Adjust the value of inertia weight according to the number of iterations. In order to solve the problem that the greater the ranging error is, the improved fitness function is proposed, and the least square fitness function is used to replace the fitness function of mean square error. Simulation results show that the improved algorithm can not only avoid falling into local optimal value and reduce the number of iterations, but also reduce the influence of ranging error on location error. Finally, the indoor network location method based on heterogeneous network is studied. According to the complementary advantages of WLAN and ZigBee network localization, a cooperative location algorithm based on ZigBee weighted centroid and WLAN particle swarm optimization is proposed. In order to reduce the searching range of particle swarm, the initial value of weighted centroid method is used as the initial position region of particle swarm. Simulation results show that the proposed algorithm not only improves the positioning accuracy and reduces the initial region of PSO, but also reduces the number of iterations and the complexity of the algorithm.
【學(xué)位授予單位】：遼寧工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN92

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 孫瑋琢;遲衛(wèi);王擎宇;;加權(quán)質(zhì)心定位誤差的反向修正法[J];傳感技術(shù)學(xué)報(bào);2012年01期

2 徐玉濱;鄧志安;馬琳;;基于核直接判別分析和支持向量回歸的WLAN室內(nèi)定位算法[J];電子與信息學(xué)報(bào);2011年04期

3 于秦;肖志輝;姜微;;基于WLAN和WSN的異構(gòu)網(wǎng)絡(luò)協(xié)同定位[J];電信科學(xué);2013年07期

4 唐笑謀;唐佳杰;;基于仿射傳播聚類的概率分布室內(nèi)定位算法[J];電信工程技術(shù)與標(biāo)準(zhǔn)化;2013年08期

5 程遠(yuǎn)國(guó);李煜;徐輝;;基于多元高斯概率分布的無(wú)線局域網(wǎng)定位方法研究[J];海軍工程大學(xué)學(xué)報(bào);2007年05期

6 李文杰;李方方;魏紅;;基于支持向量機(jī)的位置相關(guān)計(jì)算[J];計(jì)算機(jī)仿真;2008年02期

7 黃艷;臧傳治;于海斌;;基于改進(jìn)粒子群優(yōu)化的無(wú)線傳感器網(wǎng)絡(luò)定位算法[J];控制與決策;2012年01期

8 姜樂(lè)水;;淺談無(wú)線局域網(wǎng)(WLAN)技術(shù)[J];信息技術(shù)與信息化;2012年05期

9 楊新宇;孔慶茹;戴湘軍;;一種改進(jìn)的加權(quán)質(zhì)心定位算法[J];西安交通大學(xué)學(xué)報(bào);2010年08期

10 楊萍;李智;;一種基于RSSI的權(quán)重值選擇及加權(quán)定位算法[J];信息與電子工程;2012年02期

相關(guān)博士學(xué)位論文 前2條

1 張明華;基于WLAN的室內(nèi)定位技術(shù)研究[D];上海交通大學(xué);2009年

2 陳淼;基于信號(hào)強(qiáng)度的WLAN室內(nèi)定位跟蹤系統(tǒng)研究[D];武漢大學(xué);2012年

相關(guān)碩士學(xué)位論文 前2條

1 謝代軍;無(wú)線局域網(wǎng)室內(nèi)定位技術(shù)研究[D];解放軍信息工程大學(xué);2013年

2 張興;WLAN室內(nèi)定位信號(hào)特征提取算法研究[D];哈爾濱工業(yè)大學(xué);2013年

本文編號(hào)：1975745