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  本文關(guān)鍵詞： Wi-Fi指紋定位 KNN PIT 虛擬參考點(diǎn) 自適應(yīng)　出處：《江西師范大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著移動通信和互聯(lián)網(wǎng)技術(shù)的高速發(fā)展,基于位置服務(wù)(Location Based Services,LBS)的應(yīng)用需求日趨強(qiáng)烈。由于全球?qū)Ш叫l(wèi)星系統(tǒng)(Global Navigation Satellite System,GNSS)可以提供連續(xù)、高精度的室外位置信息,實(shí)現(xiàn)了諸如車輛跟蹤、車輛及行人導(dǎo)航等室外位置服務(wù)。但是在室內(nèi)復(fù)雜多變的環(huán)境中,GNSS因信號減弱或衰竭無法導(dǎo)航定位,因而高精度的室內(nèi)定位技術(shù)成為研究熱點(diǎn)。因此,基于短距離無線通信的的室內(nèi)定位方案應(yīng)運(yùn)而生。由于Wi-Fi是無線通信標(biāo)準(zhǔn),具有傳輸距離遠(yuǎn)、信號保真度高、移動性強(qiáng)、組網(wǎng)便捷等特點(diǎn),并且在大型公共場所等室內(nèi)環(huán)境已經(jīng)廣泛部署,基于Wi-Fi指紋的定位技術(shù)成為室內(nèi)LBS應(yīng)用中定位技術(shù)的首選。但是當(dāng)前Wi-Fi指紋定位方案仍存在一些問題以待解決,如信號在室內(nèi)傳播中受多徑效應(yīng)、非視距等因素導(dǎo)致的時變性影響定位的可靠性;參考點(diǎn)劃分的密集程度決定算法的復(fù)雜度,影響定位的實(shí)時性;傳統(tǒng)的KNN定位算法只能粗略估計(jì)定位點(diǎn)的位置范圍,不能對定位點(diǎn)范圍進(jìn)一步約束。針對以上問題,本文提出一種改進(jìn)的KNN—三角形內(nèi)點(diǎn)(KNN—PIT)室內(nèi)定位算法。主要工作及創(chuàng)新點(diǎn)如下:(1)根據(jù)室內(nèi)空間結(jié)構(gòu)特征,建立具有類標(biāo)號的位置指紋庫。傳統(tǒng)的指紋庫僅包含位置和對應(yīng)的接收信號強(qiáng)度指示(Received Signal Strength Indication,RSSI)向量。在指紋庫中增加類標(biāo)號位置屬性,有助于縮減定位的匹配區(qū)域,降低算法復(fù)雜度。(2)引入虛擬參考點(diǎn),利用最佳三角形內(nèi)點(diǎn)(point in triangulation,PIT)原理進(jìn)一步約束目標(biāo)點(diǎn)的定位區(qū)域,自適應(yīng)地使用定位算法進(jìn)行定位。虛擬參考點(diǎn)并不在指紋庫中真實(shí)存在,它是在KNN算法定位時假定出來,不僅有助于提高定位精度,也有助于降低指紋庫容量,降低計(jì)算復(fù)雜度。(3)綜合運(yùn)用高斯濾波、均值濾波技術(shù),降低離線和在線階段的信號隨機(jī)誤差帶來的定位影響。離線階段對采集到的大樣本W(wǎng)i-Fi信號數(shù)據(jù)進(jìn)行高斯濾波處理,去除誤差較大的干擾值。在線階段采用均值濾波降低信號的單次隨機(jī)誤差影響。最后,通過實(shí)驗(yàn)結(jié)果表明:改進(jìn)后的KNN-PIT定位算法與傳統(tǒng)KNN定位算法相比可以更好地估計(jì)用戶的實(shí)際位置,降低定位誤差,提高定位實(shí)時性。
[Abstract]:With the rapid development of mobile communications and Internet technology, the demand for location-based Based services (LBSs) applications is increasing. Because Global Navigation Satellite system (GNSS) can provide continuous and high precision outdoor location information, Outdoor location services such as vehicle tracking, vehicle and pedestrian navigation are realized. However, in the complex and changeable indoor environment, GNSS is unable to locate because of signal weakening or failure, so high-precision indoor positioning technology has become a research hotspot. The indoor positioning scheme based on short range wireless communication emerges as the times require. Because Wi-Fi is the wireless communication standard, it has the characteristics of long transmission distance, high signal fidelity, strong mobility, convenient networking, etc. The localization technology based on Wi-Fi fingerprint has become the first choice in indoor LBS application. However, there are still some problems in the current Wi-Fi fingerprint location scheme to be solved. For example, the reliability of localization is affected by multipath effect and non-line-of-sight effect, the complexity of the algorithm is determined by the density of reference points, and the real time of location is influenced by the density of reference points. The traditional KNN localization algorithm can only roughly estimate the location range of the location point, but can not further constrain the location point range. In this paper, an improved KNN- triangle interior point KNN-PIT-based indoor location algorithm is proposed. The main work and innovation are as follows: 1) according to the characteristics of indoor spatial structure, The traditional fingerprint database contains only the location and the corresponding received signal strength indication received Signal Strength indication RSSI vector. Adding the class label position attribute to the fingerprint database can help to reduce the matching area of the location. In order to reduce the complexity of the algorithm, the virtual reference point is introduced, and the location area of the target point is further constrained by the principle of the optimal triangle interior point in triangulation site, and the location algorithm is used adaptively. The virtual reference point does not exist in the fingerprint database. It is assumed in the localization of KNN algorithm, which not only helps to improve the accuracy of location, but also helps to reduce the capacity of fingerprint database and reduce the computational complexity. To reduce the impact of random errors in off-line and on-line signal positioning. Gao Si filter is used to process the large sample Wi-Fi signal data in off-line phase. In the online stage, the mean filter is used to reduce the single random error of the signal. Finally, The experimental results show that the improved KNN-PIT localization algorithm can better estimate the actual location of the user, reduce the positioning error and improve the real-time location than the traditional KNN location algorithm.
【學(xué)位授予單位】：江西師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN92

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1 唐瑞;基于Wi-Fi的KNN-PIT室內(nèi)自適應(yīng)指紋定位技術(shù)研究[D];江西師范大學(xué);2016年

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本文編號：1530640