【摘要】：隨著室內(nèi)環(huán)境中基于位置服務(wù)的需求快速的增長(zhǎng),基于指紋識(shí)別的室內(nèi)定位因其較高的精度引起了廣泛的關(guān)注。接收信號(hào)強(qiáng)度指示(RSSI)作為一種常規(guī)的方案被廣泛的用于位置導(dǎo)航系統(tǒng)和定位系統(tǒng),但是室內(nèi)復(fù)雜環(huán)境產(chǎn)生的多徑效應(yīng)導(dǎo)致系統(tǒng)的精確性得不到保障。近年來(lái),物理層的信道狀態(tài)信息(CSI)能夠被更多的無(wú)線商用設(shè)備獲取,它能更細(xì)粒度展現(xiàn)信號(hào)的特征,而且擁有更好的穩(wěn)定性。本文中,提出了一種基于CSI指紋的室內(nèi)被動(dòng)定位算法,能更加精確的估計(jì)出目標(biāo)的具體坐標(biāo)位置。首先采用基于密度的聚類算法DBSCAN去除原始數(shù)據(jù)中的噪點(diǎn),降低離群數(shù)據(jù)的干擾;然后使用主成分分析法(PCA)提取特征中貢獻(xiàn)率高的項(xiàng)目,降低特征維度和計(jì)算復(fù)雜度;最后結(jié)合支持向量機(jī)(SVM)的回歸算法建立CSI指紋與位置坐標(biāo)的關(guān)聯(lián)模型。同時(shí),還將CSI指紋運(yùn)用于行為監(jiān)測(cè),在入侵檢測(cè)中使用SVM的二分類方法檢測(cè)入侵的發(fā)生;在簡(jiǎn)單目標(biāo)識(shí)別中使用SVM的多分類方法區(qū)分目標(biāo);在室內(nèi)目標(biāo)計(jì)數(shù)中使用基于權(quán)值的膨脹矩陣法結(jié)合SVM回歸算法計(jì)算目標(biāo)個(gè)數(shù);在人群密度檢測(cè)中使用動(dòng)態(tài)時(shí)間歸整(DTW)算法匹配最佳的人群密度。實(shí)驗(yàn)結(jié)果顯示,本文提出的定位算法平均定位誤差距離為1.37米,通過(guò)與多種定位方法對(duì)比,證明該方法在定位的精度上有明顯的優(yōu)勢(shì);在入侵檢測(cè)中,門口入侵檢測(cè)和房間有人檢測(cè)的準(zhǔn)確度分別達(dá)到98.2%和99.1%;簡(jiǎn)單目標(biāo)識(shí)別中分類準(zhǔn)確率為98.7%;室內(nèi)目標(biāo)計(jì)數(shù)的平均數(shù)目誤差數(shù)量為0.62;人群密度的準(zhǔn)確率為95%。實(shí)驗(yàn)證明本文提出的基于CSI指紋的行為監(jiān)測(cè)切實(shí)可用,可實(shí)現(xiàn)行為的準(zhǔn)確監(jiān)測(cè)。
[Abstract]:With the rapid growth of the demand for location-based services in indoor environment, fingerprint based indoor location has attracted wide attention due to its high accuracy. As a conventional scheme, the received signal intensity indication (RSSI) is widely used in position navigation systems and positioning systems, but the accuracy of the system is not guaranteed due to the multipath effect caused by complex indoor environment. In recent years, the physical layer channel state information (CSI) can be obtained by more wireless commercial devices, it can show the characteristics of the signal more fine-grained, and has better stability. In this paper, an indoor passive location algorithm based on CSI fingerprint is proposed, which can estimate the exact position of the target more accurately. Firstly, the density based clustering algorithm (DBSCAN) is used to remove the noise in the original data to reduce the disturbance of outlier data, and then the principal component analysis (PCA) is used to extract the items with high contribution rate to reduce the feature dimension and computational complexity. Finally, combining the regression algorithm of support vector machine (SVM), the correlation model between CSI fingerprint and position coordinates is established. At the same time, the CSI fingerprint is applied to behavior monitoring, and the two-classification method of SVM is used to detect the occurrence of intrusion in intrusion detection, and the multi-classification method of SVM is used to distinguish the object in simple target recognition. In the indoor target counting, the expansion matrix method based on weight and the SVM regression algorithm are used to calculate the number of targets, and the dynamic time normalized (DTW) algorithm is used to match the optimal population density in crowd density detection. The experimental results show that the average positioning error distance of the proposed algorithm is 1.37 meters. Compared with many localization methods, this method has obvious advantages in positioning accuracy. In intrusion detection, the accuracy of door intrusion detection and room human detection is 98.2% and 99.1 respectively, the classification accuracy of simple target recognition is 98.7, the average number of errors of indoor target count is 0.62. The accuracy of crowd density was 95%. The experimental results show that the proposed CSI fingerprint based behavior monitoring is feasible and can be used to accurately monitor behavior.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN911.7

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本文編號(hào)：2338372