【摘要】：隨著無(wú)線網(wǎng)絡(luò)技術(shù)的發(fā)展與現(xiàn)代化城市建設(shè)的快速發(fā)展,基于位置感知的服務(wù)(Location Based Services,LBS)在個(gè)人位置服務(wù)、醫(yī)療領(lǐng)域、電子商務(wù)、緊急救援、智能家居等多方面顯示出巨大的活力,是近年來(lái)備受關(guān)注的一大研究熱點(diǎn)。而具有較高精度的室內(nèi)定位與導(dǎo)航技術(shù)是實(shí)現(xiàn)LBS的基礎(chǔ)與關(guān)鍵。傳統(tǒng)的全球定位系統(tǒng)GPS(GlobalPositioningSystem)和蜂窩移動(dòng)通信技術(shù)在室外時(shí)擁有較高的定位精度,但是在室內(nèi)環(huán)境中GPS信號(hào)會(huì)受到遮擋,導(dǎo)致定位精度大大下降。相比于已有的室內(nèi)定位技術(shù),從部署成本、定位精度、后期維護(hù)、傳輸速度、可移植性等方面綜合考慮,基于WiFi(WirelessFidelity)接收信號(hào)強(qiáng)度(Received Signal Strength,RSS)的室內(nèi)定位技術(shù)不需要布設(shè)其他硬件設(shè)備,通過(guò)充分利用已有的WiFi設(shè)施,即可在任何具有WiFi模塊的智能移動(dòng)設(shè)備上實(shí)現(xiàn)定位,在眾多定位算法中具有一定的優(yōu)勢(shì)。然而,RSS容易受到外界環(huán)境干擾,嚴(yán)重影響室內(nèi)定位系統(tǒng)的穩(wěn)定性與準(zhǔn)確性,單純的基于WiFi信號(hào)的定位無(wú)法滿足人們對(duì)室內(nèi)定位服務(wù)的精度要求。針對(duì)上述問(wèn)題,本文通過(guò)對(duì)WiFi信號(hào)接收強(qiáng)度的特征分析,提出基于信息融合的室內(nèi)定位算法,將基于RSS的WiFi位置指紋定位算法與行人航位推算算法(Pedestrian Dead Reckoning,PDR)通過(guò)Kalman濾波器進(jìn)行數(shù)據(jù)融合實(shí)現(xiàn)定位,并在智能移動(dòng)終端上開(kāi)發(fā)實(shí)現(xiàn)了集室內(nèi)定位、導(dǎo)航、追蹤為一體的應(yīng)用系統(tǒng)。本文的主要內(nèi)容和創(chuàng)新點(diǎn)包括:(1)三維室內(nèi)空間模型的構(gòu)建。結(jié)構(gòu)清晰、具有良好的表達(dá)能力和視覺(jué)效果的室內(nèi)空間模型是實(shí)現(xiàn)室內(nèi)LBS的基礎(chǔ)。相比于室外環(huán)境,室內(nèi)空間結(jié)構(gòu)的復(fù)雜性對(duì)室內(nèi)建模提出很大的挑戰(zhàn)。本文根據(jù)現(xiàn)有的室內(nèi)數(shù)據(jù)文件,設(shè)計(jì)并構(gòu)建了基于"結(jié)點(diǎn)-弧段"結(jié)構(gòu)的三維室內(nèi)空間網(wǎng)絡(luò)模型,用以表達(dá)室內(nèi)空間要素的空間屬性與拓?fù)浣Y(jié)構(gòu),作為地圖可視化與室內(nèi)導(dǎo)航的基礎(chǔ)。并進(jìn)一步具體論述了基于Voronoi圖的室內(nèi)走廊中軸線提取原理,實(shí)現(xiàn)了建筑單層路徑的自動(dòng)提取,提高了建模的效率。(2)改進(jìn)的基于RSS的位置指紋定位方法。本文通過(guò)對(duì)RSS的深入研究,從室內(nèi)定位的角度分析了不同因素對(duì)RSS的影響。并針對(duì)RSS的復(fù)雜性與多變性特點(diǎn),提出基于空間收斂的WKNN(Weighted K-Nearest Neighbor)室內(nèi)定位算法,實(shí)現(xiàn)了較為精確地室內(nèi)定位。同時(shí),通過(guò)采用不同的接入點(diǎn)(AccessPoint,AP)選擇和匹配機(jī)制,去除冗余的AP數(shù)據(jù)并優(yōu)化AP定位子集合,提高定位算法的效率與精度。通過(guò)與同機(jī)制下的算法進(jìn)行比較,本文提出的算法在實(shí)時(shí)性和定位精度方面均有提高。在實(shí)驗(yàn)環(huán)境下,以1.5米的采樣間隔創(chuàng)建位置指紋數(shù)據(jù)庫(kù),在使用6個(gè)AP進(jìn)行定位的情況下,獲得的平均定位誤差為1.68 m。(3)基于Kalman濾波的多數(shù)據(jù)融合室內(nèi)實(shí)時(shí)追蹤與導(dǎo)航。在實(shí)時(shí)室內(nèi)導(dǎo)航過(guò)程中,基于RSS的位置指紋定位算法易受室內(nèi)環(huán)境變化的影響,存在定位不穩(wěn)定且精度不高的現(xiàn)象,對(duì)于運(yùn)動(dòng)物體的位置描述也存在不規(guī)則跳躍現(xiàn)象;而PDR算法可直接利用移動(dòng)設(shè)備自帶的傳感器,通過(guò)對(duì)行人運(yùn)動(dòng)狀態(tài)的估計(jì)進(jìn)行相對(duì)位置預(yù)測(cè),但是定位存在不可消除的累計(jì)誤差。本文建立Kalman濾波器對(duì)兩者定位信息進(jìn)行數(shù)據(jù)融合和軌跡平滑,以實(shí)現(xiàn)在室內(nèi)導(dǎo)航過(guò)程中獲得較高精度的室內(nèi)實(shí)時(shí)動(dòng)態(tài)定位精度。并通過(guò)行人軌跡方向檢測(cè),在轉(zhuǎn)彎處對(duì)Kalman濾波進(jìn)行重置,來(lái)降低線性運(yùn)動(dòng)模型在轉(zhuǎn)彎處的累積定位誤差。同時(shí),文中利用設(shè)備氣壓計(jì)數(shù)據(jù)來(lái)識(shí)別導(dǎo)航過(guò)程中用戶的上下樓行為,實(shí)現(xiàn)了適用于智能移動(dòng)設(shè)備的多樓層的定位與導(dǎo)航系統(tǒng)。通過(guò)實(shí)驗(yàn)驗(yàn)證,在室內(nèi)動(dòng)態(tài)追蹤與導(dǎo)航過(guò)程中系統(tǒng)的平均定位誤差為1.2m。將算法結(jié)果與PDR、WiFi定位進(jìn)行對(duì)比,本算法在隨著時(shí)間上的累積誤差上表現(xiàn)最為平穩(wěn)。
[Abstract]:With the development of wireless network technology and the rapid development of modern city construction, Location Based Services (LBS) has shown tremendous vitality in many aspects, such as personal location service, medical field, electronic commerce, emergency rescue, smart home and so on. It has become a hot research topic in recent years. High-precision indoor positioning and navigation technology is the foundation and key to realize LBS. Traditional GPS (Global Positioning System) and cellular mobile communication technology have higher positioning accuracy outdoors, but GPS signals in indoor environment will be blocked, resulting in a significant reduction in positioning accuracy. Bit technology, from the deployment cost, positioning accuracy, post-maintenance, transmission speed, portability and other aspects of a comprehensive consideration, based on WiFi (Wireless Fidelity) Received Signal Strength (RSS) indoor positioning technology does not require the deployment of other hardware devices, by making full use of existing WiFi facilities, you can have any WiFi module. However, RSS is susceptible to external environment interference, which seriously affects the stability and accuracy of indoor positioning system. Simple WiFi-based positioning can not meet the accuracy requirements of indoor positioning services. Based on the analysis of the characteristics of the reception intensity of WiFi signals, an indoor localization algorithm based on information fusion is proposed. The location algorithm of WiFi position fingerprint based on RSS and Pedestrian Dead Reckoning (PDR) are fused by Kalman filter to realize the localization, and the indoor localization is realized on the intelligent mobile terminal. The main contents and innovations of this paper include: (1) the construction of three-dimensional indoor space model. The indoor space model with clear structure, good expressive ability and visual effect is the foundation of indoor LBS. Compared with outdoor environment, the complexity of indoor space structure has a great impact on indoor modeling. According to the existing indoor data files, this paper designs and constructs a three-dimensional indoor space network model based on "node-arc" structure to express the spatial attributes and topological structure of indoor space elements, which is the basis of map visualization and indoor navigation. The principle of axes extraction realizes the automatic extraction of building single-layer path and improves the efficiency of modeling. (2) An improved location fingerprint method based on RSS is proposed. WKNN (Weighted K-Nearest Neighbor) indoor localization algorithm based on WKNN (Weighted K-Nearest Neighbor) achieves more accurate indoor localization. At the same time, by using different access point (AP) selection and matching mechanism, redundant AP data is removed and AP localization subset is optimized to improve the efficiency and accuracy of localization algorithm. The algorithm presented in this paper improves the real-time performance and positioning accuracy. In the experimental environment, a location fingerprint database is created with a sampling interval of 1.5 meters, and the average positioning error is 1.68 m when six APs are used for positioning. (3) Multi-data fusion indoor real-time tracking and navigation based on Kalman filtering. In the navigation process, the location fingerprint localization algorithm based on RSS is vulnerable to the influence of indoor environment changes, there is instability and low precision in the location, and there is also irregular jumping phenomenon in the position description of moving objects; PDR algorithm can directly use the sensors of mobile devices to estimate the state of pedestrian movement. In this paper, Kalman filter is established to fuse the positioning information and smooth the trajectory, so as to achieve high precision indoor real-time dynamic positioning accuracy in the process of indoor navigation. In order to reduce the cumulative positioning error of the linear motion model at the turning point, the device barometer data is used to identify the user's upstairs and downstairs behavior in the navigation process, and the multi-floor positioning and navigation system suitable for intelligent mobile devices is realized. The average positioning error is 1.2m. Compared with PDR and WiFi, the algorithm is the most stable in cumulative error over time.
【學(xué)位授予單位】：華東師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN92

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