【摘要】：地理圍欄技術(shù)(Geo-fencing)在信息推送、智能家居、考勤簽到、兒童安全監(jiān)控以及智慧醫(yī)療等領(lǐng)域有著十分重要的應(yīng)用。地理圍欄的核心是定位技術(shù),GPS占主導(dǎo)的室外定位研究已相對成熟,但是在室內(nèi)、地下通道等復(fù)雜易變環(huán)境下,無法通過GPS來提供高精度連續(xù)導(dǎo)航需求。大規(guī)模普及的移動終端已經(jīng)成為應(yīng)用最為廣泛的導(dǎo)航終端設(shè)備,研究基于移動終端傳感器的室內(nèi)導(dǎo)航技術(shù)應(yīng)用在室內(nèi)地理圍欄領(lǐng)域具有很好的前景。然而基于移動終端傳感器的室內(nèi)地理圍欄的挑戰(zhàn)有:移動終端傳感器信號采樣頻率低和周圍環(huán)境或者行人活動對傳感器干擾大;持移動終端姿勢繁雜、運(yùn)動自由度高,移動終端方向傳感器航向并不等同于行人航向;移動終端功耗和性能有限;室內(nèi)虛擬地理圍欄要求終端具備持續(xù)高精度導(dǎo)航能力。首先分析了各種定位技術(shù)的優(yōu)劣勢,充分考慮到上述移動終端傳感器室內(nèi)地理圍欄的挑戰(zhàn),本文采用行人航跡推算方法,并且把它應(yīng)用在室內(nèi)地理圍欄模型中,通過分析移動終端傳感器信號特征,依據(jù)行人運(yùn)動生理學(xué)特性,推算行人持移動終端在各種應(yīng)用場景下的步頻、步長和航向,計算行人在室內(nèi)平面活動位移,'實時監(jiān)測行人是否離開虛擬地理圍欄區(qū)域;其次針對傳統(tǒng)行人航跡航向算法初始化校準(zhǔn)、誤差動態(tài)補(bǔ)償、亞穩(wěn)定場景下誤差模型等弊端,不適用于室內(nèi)復(fù)雜環(huán)境中姿勢繁雜和運(yùn)動高度自由的行人移動終端,本文在調(diào)研室內(nèi)平面98%區(qū)域布局呈現(xiàn)規(guī)則的方角特性后,提出通過小波變換分析移動終端方向傳感器信號特征,然后使用神經(jīng)網(wǎng)絡(luò)半監(jiān)督學(xué)習(xí)模型預(yù)測用戶在各種使用場景中的航向角方法。實驗結(jié)果表明基于移動終端傳感器的室內(nèi)地理圍欄解決方案可行,當(dāng)用戶在室內(nèi)虛擬圍欄區(qū)域活動15～60分鐘內(nèi)時,地理圍欄的實時準(zhǔn)確率達(dá)到93.5%,當(dāng)延時3秒激活通知用戶事件時,地理圍欄的準(zhǔn)確率達(dá)到98%,而且改進(jìn)后的航向預(yù)測精確度達(dá)到96.6%。
[Abstract]:Geographic fence technology (Geo-fencing) has important applications in the fields of information push, smart home, attendance check in, child safety monitoring and intelligent medical treatment. The core of the geographical fence is the positioning technology. The research on GPS-dominated outdoor positioning has been relatively mature, but in the complex and changeable environment, such as indoor and underground passage, it is impossible to provide high precision continuous navigation through GPS. Large-scale mobile terminal has become the most widely used navigation terminal equipment. The research of indoor navigation technology based on mobile terminal sensor has a good prospect in the field of indoor geographical fence. However, the challenges of indoor geographic fence based on mobile terminal sensor are: low sampling frequency of mobile terminal sensor signal, large disturbance to sensor by surrounding environment or pedestrian activity, complex posture of holding mobile terminal and high degree of freedom of movement. The heading of mobile terminal direction sensor is not equal to pedestrian heading; the power consumption and performance of mobile terminal are limited; the indoor virtual geographic fence requires the terminal to have continuous high precision navigation capability. Firstly, the advantages and disadvantages of various positioning techniques are analyzed, and the challenges of the indoor geographic fence of the mobile terminal sensor are fully considered. In this paper, the pedestrian track estimation method is adopted, and it is applied to the indoor geographical fence model. According to the physiological characteristics of pedestrian movement, the step frequency, step size and heading of the mobile terminal in various application scenarios are calculated by analyzing the signal characteristics of the sensor in the mobile terminal. Calculating the pedestrian displacement in the indoor plane and monitoring whether the pedestrian leaves the virtual geographic fence in real time; secondly, aiming at the disadvantages of the traditional pedestrian course algorithm initialization and calibration, the error dynamic compensation, the error model under the sub-stable scene, and so on. It is not suitable for pedestrian mobile terminals with complicated posture and high freedom of movement in complex indoor environment. In this paper, wavelet transform is used to analyze the signal features of the mobile terminal direction sensor, and then the neural network semi-supervised learning model is used to predict the course angle of the user in various usage scenarios. The experimental results show that the indoor geographic fence solution based on mobile terminal sensor is feasible. When the user moves in the indoor virtual fence area within 1560 minutes, The real-time accuracy of the geographic fence reaches 93.5. when the delay of 3 seconds activates notifying the user, the accuracy of the geographic fence reaches 98, and the precision of the improved course prediction reaches 96.6.
【學(xué)位授予單位】：華中師范大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 陳進(jìn);王坤;李耀明;;基于Mallat算法的谷物流量信號小波去噪方法[J];農(nóng)業(yè)工程學(xué)報;2017年03期

相關(guān)博士學(xué)位論文 前1條

1 陳偉;基于GPS和自包含傳感器的行人室內(nèi)外無縫定位算法研究[D];中國科學(xué)技術(shù)大學(xué);2010年

相關(guān)碩士學(xué)位論文 前1條

1 馬嘉斌;基于便攜設(shè)備行人航位推算的室內(nèi)定位研究[D];上海交通大學(xué);2014年

，

本文編號：2199035