【摘要】：礦山物聯(lián)網(wǎng)技術(shù)的發(fā)展推動(dòng)了Wi Fi技術(shù)在煤礦井下的應(yīng)用。如何在Wi Fi網(wǎng)絡(luò)上實(shí)現(xiàn)定位功能,用以實(shí)現(xiàn)礦工位置信息的跟蹤成為目前研究熱點(diǎn)之一。針對(duì)現(xiàn)有地面室內(nèi)定位技術(shù)在煤礦井下定位效果不佳。本學(xué)位論文對(duì)基于Wi Fi網(wǎng)絡(luò)的煤礦井下定位進(jìn)行了研究。本文首先對(duì)基于Wi Fi網(wǎng)絡(luò)的定位算法進(jìn)行了簡(jiǎn)介,在對(duì)各種算法的適用性比較的基礎(chǔ)上,指出煤礦井下定位應(yīng)采用基于場(chǎng)景的定位算法。傳統(tǒng)的場(chǎng)景定位算法利用指紋匹配的思想進(jìn)行定位,本文利用煤礦井下場(chǎng)景定位的特點(diǎn),將統(tǒng)計(jì)機(jī)器學(xué)習(xí)理論引入到定位中,使用支持向量機(jī),將場(chǎng)景定位中的指紋匹配問題轉(zhuǎn)換成支持向量機(jī)中的分類問題。針對(duì)分類的準(zhǔn)確性問題。對(duì)如何優(yōu)化支持向量機(jī)參數(shù)進(jìn)行了研究,并利用啟發(fā)式算法優(yōu)化對(duì)支持向量機(jī)參數(shù)進(jìn)行優(yōu)化。通過仿真分析看出,通過啟發(fā)式算法優(yōu)化后的支持向量機(jī),分類準(zhǔn)確度最高可以達(dá)到98.88%。在對(duì)井下實(shí)際定位場(chǎng)景環(huán)境分析的基礎(chǔ)上,本文提出了基于網(wǎng)格劃分的分層定位算法。算法實(shí)現(xiàn)定位從大范圍到小區(qū)域的逐步精化。該算法與傳統(tǒng)場(chǎng)景定位算法相比,充分發(fā)揮了傳統(tǒng)場(chǎng)景算法優(yōu)勢(shì),又有效避開了傳統(tǒng)算法的不足,實(shí)驗(yàn)結(jié)果說明該算法可以獲得更好定位精度和穩(wěn)定性,與常用的定位系統(tǒng)相比算法平均定位精度提高約10%。
[Abstract]:The development of mine Internet of things technology has promoted the application of Wi Fi technology in coal mine. How to realize the location function on Wi Fi network and how to track the miners' position information has become one of the research hotspots. In view of the existing surface indoor positioning technology in coal mine underground positioning effect is not good. In this thesis, the location of underground coal mine based on Wi Fi network is studied. In this paper, the localization algorithm based on Wi Fi network is introduced. Based on the comparison of the applicability of various algorithms, it is pointed out that the location algorithm should be based on scene. The traditional scene location algorithm uses the idea of fingerprint matching to locate. In this paper, the statistical machine learning theory is introduced into the location, and the support vector machine is used. The fingerprint matching problem in scene location is transformed into a classification problem in support vector machine (SVM). Aim at the accuracy of classification. This paper studies how to optimize the parameters of support vector machine, and uses heuristic algorithm to optimize the parameters of support vector machine. The simulation results show that the classification accuracy can reach 98.88 by the optimized support vector machine based on heuristic algorithm. Based on the analysis of the environment of the downhole actual location scene, this paper presents a hierarchical localization algorithm based on grid division. The algorithm realizes the gradual refinement of localization from a large area to a small area. Compared with the traditional scene location algorithm, this algorithm has the advantage of the traditional scene algorithm, and effectively avoids the shortcomings of the traditional algorithm. The experimental results show that the algorithm can achieve better positioning accuracy and stability. Compared with the common positioning system, the average positioning accuracy of the algorithm is improved by about 10%.
【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】：2015
【分類號(hào)】：TD65

【參考文獻(xiàn)】

相關(guān)期刊論文 前5條

1 方爽;郭杭;洪海斌;李英成;;一種虛擬空間劃分的室內(nèi)指紋庫(kù)定位方法[J];測(cè)繪科學(xué);2015年01期

2 尚二花;;淺談霍爾辛赫煤礦長(zhǎng)距離工作面貫通經(jīng)驗(yàn)[J];技術(shù)與市場(chǎng);2014年09期

3 申寶宏;劉見中;雷毅;;我國(guó)煤礦區(qū)煤層氣開發(fā)利用技術(shù)現(xiàn)狀及展望[J];煤炭科學(xué)技術(shù);2015年02期

4 都伊林;;一種模糊聚類KNN位置指紋定位算法[J];微型機(jī)與應(yīng)用;2012年23期

5 李晶;李冬海;趙擁軍;;利用角度和時(shí)差的單站外輻射源定位方法[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2015年02期

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本文編號(hào)：2396995