【摘要】：隨著無(wú)線局域網(wǎng)(Wireless Local Area Networks,WLAN)的覆蓋和4G網(wǎng)絡(luò)的推廣,人們能隨時(shí)、隨地、高速的接入互聯(lián)網(wǎng),獲取自己需要的信息,諸多基于位置信息的應(yīng)用也應(yīng)運(yùn)而生,新的問(wèn)題也隨之而來(lái)。由于不同移動(dòng)終端硬件及實(shí)現(xiàn)差異,導(dǎo)致在定位過(guò)程中處于同一位置的移動(dòng)終端采集到的無(wú)線訪問(wèn)接入點(diǎn)(Access Point,AP)信號(hào)強(qiáng)度(Received Signal Strength Indication,RSSI)不同,引起定位誤差。為此,本論文首先在實(shí)際室內(nèi)環(huán)境中采集WiFi數(shù)據(jù),分析移動(dòng)終端設(shè)備之間的差異性。在分析過(guò)程中,發(fā)現(xiàn)移動(dòng)終端設(shè)備所獲得的RSSI數(shù)值異常值,與環(huán)境瞬間變化有關(guān)�；诖�,本文通過(guò)設(shè)定置信區(qū)間來(lái)修正同一AP獲得的RSSI異常值,來(lái)提高定位準(zhǔn)確度。其次,探索解決終端設(shè)備差異性問(wèn)題的解決方案。通過(guò)對(duì)比不同設(shè)備在多時(shí)段所采集的RSSI數(shù)據(jù),發(fā)現(xiàn)當(dāng)不同設(shè)備在同一位置采集周?chē)鶤P無(wú)線信號(hào)時(shí),不同設(shè)備所采集的RSSI數(shù)值趨勢(shì)大致相似。利用上述發(fā)現(xiàn),借鑒加權(quán)K近鄰(Weighted K-nearest Neighbor,WKNN)算法中權(quán)重因子的概念并結(jié)合Pearson相關(guān)系數(shù),提出一種基于Pearson相似度的終端差異消除方法。在該算法中需要計(jì)算定位設(shè)備所采集的數(shù)據(jù)與位置指紋數(shù)據(jù)庫(kù)內(nèi)每一個(gè)采樣點(diǎn)指紋數(shù)據(jù)之間的Pearson相關(guān)程度,并將計(jì)算所得的結(jié)果作為一個(gè)系數(shù)因子,解決不同設(shè)備所獲得的RSSI數(shù)據(jù)不匹配的問(wèn)題。最后,在基于Android的平臺(tái)上驗(yàn)證了提出的基于Pearson相似度的終端差異消除方法的有效性。實(shí)驗(yàn)將三款不同智能終端設(shè)備分別與不同的現(xiàn)有算法相比較,發(fā)現(xiàn)本文提出的方法有效地降低了終端設(shè)備差異性,減少了室內(nèi)定位誤差,提高了室內(nèi)定位準(zhǔn)確性。實(shí)驗(yàn)結(jié)果表明,對(duì)于同構(gòu)設(shè)備,基于Pearson相似度的終端差異消除方法在1.5m以?xún)?nèi)的定位精度保持在85%以上;對(duì)于異構(gòu)設(shè)備,定位精度保持在70%以上。
[Abstract]:With the coverage of wireless local area network (Wireless Local Area Networks,WLAN) and the promotion of 4G network, people can access the Internet at any time, anywhere and at high speed to get the information they need. Many location-based applications have emerged, and new problems have followed. Because of the difference in hardware and realization of different mobile terminals, the wireless access point (Access Point,AP) signal intensity (Received Signal Strength Indication,RSSI (Received Signal Strength Indication,RSSI) collected by the mobile terminal in the same location is different, which results in the location error. Therefore, this paper firstly collects WiFi data in the actual indoor environment and analyzes the differences between mobile terminal devices. In the process of analysis, it is found that the outliers of RSSI obtained by mobile terminal devices are related to the instantaneous change of environment. Based on this, this paper modifies the RSSI outliers obtained by the same AP by setting the confidence interval to improve the localization accuracy. Secondly, explore the solution to the terminal equipment difference problem. By comparing the RSSI data collected by different equipments in different time periods, it is found that when different devices collect the surrounding AP wireless signals in the same position, the RSSI values collected by different devices are roughly similar. Based on the above findings, using the concept of weight factor in weighted K nearest neighbor (Weighted K-nearest Neighbor,WKNN) algorithm and Pearson correlation coefficient, a terminal difference cancellation method based on Pearson similarity is proposed. In this algorithm, we need to calculate the Pearson correlation between the data collected by the location device and the fingerprint data of each sampling point in the location fingerprint database, and take the calculated results as a coefficient factor. To solve the problem of RSSI data mismatch obtained by different devices. Finally, the effectiveness of the proposed terminal difference cancellation method based on Pearson similarity is verified on the platform of Android. By comparing three different intelligent terminal devices with different existing algorithms, it is found that the method proposed in this paper can effectively reduce the difference of terminal equipment, reduce the indoor positioning error and improve the accuracy of indoor positioning. The experimental results show that for isomorphic equipment, the location accuracy of the terminal difference cancellation method based on Pearson similarity is more than 85% within 1.5m, and that of heterogeneous equipment is more than 70%.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TN925.93

【參考文獻(xiàn)】

相關(guān)期刊論文 前8條

1 薛衛(wèi)星;邱衛(wèi)寧;花向紅;吳幫;蔡建民;;RSSI信號(hào)特征值對(duì)WiFi室內(nèi)定位精度的影響分析[J];測(cè)繪地理信息;2016年04期

2 曹世華;;室內(nèi)定位技術(shù)和系統(tǒng)的研究進(jìn)展[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2013年09期

3 李擁軍;曾標(biāo);徐克付;李陽(yáng);;復(fù)雜背景下基于貝葉斯-全概率聯(lián)合估計(jì)的前景檢測(cè)[J];電子與信息學(xué)報(bào);2012年02期

4 汪苑;林錦國(guó);;幾種常用室內(nèi)定位技術(shù)的探討[J];中國(guó)儀器儀表;2011年02期

5 阮衛(wèi)華;;常見(jiàn)的室內(nèi)無(wú)線定位技術(shù)簡(jiǎn)介[J];科技信息;2009年33期

6 關(guān)媛媛,安世全,劉光明;提供精確室內(nèi)定位信息的Cricket系統(tǒng)[J];重慶工業(yè)高等專(zhuān)科學(xué)校學(xué)報(bào);2004年02期

7 倪巍,王宗欣;基于接收信號(hào)強(qiáng)度測(cè)量的室內(nèi)定位算法[J];復(fù)旦學(xué)報(bào)(自然科學(xué)版);2004年01期

8 郭紅霞;;相關(guān)系數(shù)及其應(yīng)用[J];武警工程大學(xué)學(xué)報(bào);2010年02期

相關(guān)碩士學(xué)位論文 前1條

1 魏雷;WIFI位置指紋定位技術(shù)研究及仿真器設(shè)計(jì)[D];西南交通大學(xué);2012年

，

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