本文選題：行人航位推算　切入點(diǎn)：室內(nèi)地圖匹配　出處：《電子科技大學(xué)》2017年碩士論文

【摘要】：行人航位推算常用于估算用戶在室內(nèi)環(huán)境的位置,然而,傳感器測量數(shù)據(jù)偏差,傳統(tǒng)航位推算算法中誤差的積累,對最終的定位結(jié)果影響很大;其次,用戶持握方式是多變的,很難建立起傳感器數(shù)據(jù)與持握方式之間的準(zhǔn)確關(guān)系,難以根據(jù)數(shù)據(jù)特征進(jìn)行分類;再有,行人行走軌跡可能會(huì)出現(xiàn)偏移較大、穿墻而過的錯(cuò)誤現(xiàn)象。針對上述問題,本文提出一個(gè)改進(jìn)的行人航位推算算法,在穿零檢測法基礎(chǔ)之上,提出上升穿零與下降穿零兩種情況下的步數(shù)統(tǒng)計(jì)方法;接著,綜合五種變量:性別、身高、步頻、波峰加速度、波谷加速度,提出新的步長計(jì)算方法,通過多元變量線性回歸分別訓(xùn)練男、女?dāng)?shù)據(jù)樣本得到該模型,接著使用粒子濾波,修正間歇性跳躍的步長結(jié)果;然后,根據(jù)磁力計(jì)方向角和陀螺儀相對轉(zhuǎn)向角度的線性相關(guān)性,線性擬合得到基于二者角度變化值的方向估算模型,以彌補(bǔ)傳統(tǒng)航位推算算法中單獨(dú)使用磁力儀或陀螺儀進(jìn)行方向估算產(chǎn)生的大幅偏差;此外,針對不同的持握方式,根據(jù)其加速度信號,利用小波變換提取運(yùn)動(dòng)特征,奇異值分解進(jìn)行特征降維,通過支持向量機(jī)進(jìn)行分類訓(xùn)練,達(dá)到準(zhǔn)確判斷不同持握方式的目標(biāo),并根據(jù)特定持握方式下,用戶對導(dǎo)航軌跡不敏感的特點(diǎn),統(tǒng)計(jì)步數(shù)與步長;最后,本文提出室內(nèi)地圖建模與匹配方法,先對室內(nèi)空間進(jìn)行抽象、建模,再通過興趣點(diǎn)匹配、穿墻檢測、方向修正等步驟限制用戶運(yùn)動(dòng)軌跡,使其與真實(shí)軌跡更加貼近。實(shí)驗(yàn)結(jié)果表明,計(jì)步階段,穿零檢測法平均誤差0.8%,傳統(tǒng)波峰檢測法平均誤差11.6%;本文提出的步長計(jì)算誤差3.5%,相比于已有的身高-步頻模型誤差8.63%,以及波峰-波谷根式模型10.84%,有顯著提升;方向估算結(jié)果90%以內(nèi)的樣本誤差都在20°以內(nèi),對比傳統(tǒng)的磁力計(jì)、陀螺儀有明顯的優(yōu)勢;使用SVM進(jìn)行持握方式判斷的準(zhǔn)確率高達(dá)95.62%,而貝葉斯分類的準(zhǔn)確率只有82.31%。最后地圖匹配的實(shí)驗(yàn)中,繪制軌跡克服了漂移、穿墻等問題,改進(jìn)的航位推算算法平均誤差1.48m,繪制的軌跡能反映行走路線。本文出的改進(jìn)的航位推算算法及地圖匹配在室內(nèi)定位中大大提高了定位精度,具有較高實(shí)用性。
[Abstract]:Pedestrian dead-reckoning is often used to estimate the user's position in the indoor environment. However, the error accumulation in the traditional dead-reckoning algorithm has a great influence on the final positioning results. It is difficult to establish an accurate relationship between the sensor data and the holding mode, and it is difficult to classify according to the characteristics of the data. In addition, the pedestrian track may have errors of deviation and passing through the wall. In this paper, an improved algorithm is proposed to calculate the number of steps on the basis of the zero-piercing method, and then the statistical method of step number is proposed under the condition of zero passing through rise and zero down, and then five variables are synthesised: gender, height, step frequency, and so on. Wave peak acceleration, trough acceleration, a new calculation method of step size is proposed. The model is obtained by training the male and female data samples separately by linear regression of multivariate variables, and then using particle filter to modify the step result of intermittent jump; then, According to the linear correlation between the direction angle of the magnetometer and the relative steering angle of the gyroscope, a direction estimation model based on the change value of the two angles is obtained by linear fitting. In order to make up for the large deviation caused by direction estimation by using magnetic force instrument or gyroscope alone in the traditional dead-reckoning algorithm, in addition, according to the acceleration signal of different holding mode, the motion feature is extracted by wavelet transform. The singular value decomposition (SVD) is used to reduce the dimension of the feature, and the SVM is used to classify and train to judge the target of different holding mode accurately. According to the characteristic that the user is insensitive to the navigation track, the step number and step size are counted. Finally, this paper proposes a method of indoor map modeling and matching. Firstly, the indoor space is abstracted, modeled, and then the user's trajectory is restricted by the matching of interest points, the detection of the wall, the direction correction and so on. The experimental results show that, in the step stage, The average error of zero-penetrating detection method is 0.8, the average error of traditional wave peak detection method is 11.60.The error of step size calculated in this paper is 3.50.Compared with the existing height and step frequency model error 8.63, and the crest and trough root model 10.84, it has a significant improvement. The sample error within 90% of the estimated direction is less than 20 擄. Compared with the traditional magnetometer, gyroscope has obvious advantages. The accuracy of using SVM to judge the holding mode is 95.62, but the accuracy of Bayesian classification is only 82.31. Finally, in the experiment of map matching, drawing track overcomes the problems of drift and wall, etc. The average error of the improved algorithm is 1.48 m and the plotted track can reflect the walking route. The improved algorithm and map matching in this paper greatly improve the positioning accuracy and have a higher practicability in indoor positioning.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP212.9

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