【摘要】：隨著無線網(wǎng)絡技術的發(fā)展與現(xiàn)代化城市建設的快速發(fā)展,基于位置感知的服務(Location Based Services,LBS)在個人位置服務、醫(yī)療領域、電子商務、緊急救援、智能家居等多方面顯示出巨大的活力,是近年來備受關注的一大研究熱點。而具有較高精度的室內定位與導航技術是實現(xiàn)LBS的基礎與關鍵。傳統(tǒng)的全球定位系統(tǒng)GPS(GlobalPositioningSystem)和蜂窩移動通信技術在室外時擁有較高的定位精度,但是在室內環(huán)境中GPS信號會受到遮擋,導致定位精度大大下降。相比于已有的室內定位技術,從部署成本、定位精度、后期維護、傳輸速度、可移植性等方面綜合考慮,基于WiFi(WirelessFidelity)接收信號強度(Received Signal Strength,RSS)的室內定位技術不需要布設其他硬件設備,通過充分利用已有的WiFi設施,即可在任何具有WiFi模塊的智能移動設備上實現(xiàn)定位,在眾多定位算法中具有一定的優(yōu)勢。然而,RSS容易受到外界環(huán)境干擾,嚴重影響室內定位系統(tǒng)的穩(wěn)定性與準確性,單純的基于WiFi信號的定位無法滿足人們對室內定位服務的精度要求。針對上述問題,本文通過對WiFi信號接收強度的特征分析,提出基于信息融合的室內定位算法,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Kalman濾波器進行數(shù)據(jù)融合實現(xiàn)定位,并在智能移動終端上開發(fā)實現(xiàn)了集室內定位、導航、追蹤為一體的應用系統(tǒng)。本文的主要內容和創(chuàng)新點包括:(1)三維室內空間模型的構建。結構清晰、具有良好的表達能力和視覺效果的室內空間模型是實現(xiàn)室內LBS的基礎。相比于室外環(huán)境,室內空間結構的復雜性對室內建模提出很大的挑戰(zhàn)。本文根據(jù)現(xiàn)有的室內數(shù)據(jù)文件,設計并構建了基于"結點-弧段"結構的三維室內空間網(wǎng)絡模型,用以表達室內空間要素的空間屬性與拓撲結構,作為地圖可視化與室內導航的基礎。并進一步具體論述了基于Voronoi圖的室內走廊中軸線提取原理,實現(xiàn)了建筑單層路徑的自動提取,提高了建模的效率。(2)改進的基于RSS的位置指紋定位方法。本文通過對RSS的深入研究,從室內定位的角度分析了不同因素對RSS的影響。并針對RSS的復雜性與多變性特點,提出基于空間收斂的WKNN(Weighted K-Nearest Neighbor)室內定位算法,實現(xiàn)了較為精確地室內定位。同時,通過采用不同的接入點(AccessPoint,AP)選擇和匹配機制,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。通過與同機制下的算法進行比較,本文提出的算法在實時性和定位精度方面均有提高。在實驗環(huán)境下,以1.5米的采樣間隔創(chuàng)建位置指紋數(shù)據(jù)庫,在使用6個AP進行定位的情況下,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)融合室內實時追蹤與導航。在實時室內導航過程中,基于RSS的位置指紋定位算法易受室內環(huán)境變化的影響,存在定位不穩(wěn)定且精度不高的現(xiàn)象,對于運動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設備自帶的傳感器,通過對行人運動狀態(tài)的估計進行相對位置預測,但是定位存在不可消除的累計誤差。本文建立Kalman濾波器對兩者定位信息進行數(shù)據(jù)融合和軌跡平滑,以實現(xiàn)在室內導航過程中獲得較高精度的室內實時動態(tài)定位精度。并通過行人軌跡方向檢測,在轉彎處對Kalman濾波進行重置,來降低線性運動模型在轉彎處的累積定位誤差。同時,文中利用設備氣壓計數(shù)據(jù)來識別導航過程中用戶的上下樓行為,實現(xiàn)了適用于智能移動設備的多樓層的定位與導航系統(tǒng)。通過實驗驗證,在室內動態(tài)追蹤與導航過程中系統(tǒng)的平均定位誤差為1.2m。將算法結果與PDR、WiFi定位進行對比,本算法在隨著時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.
【學位授予單位】：華東師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TN92

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