本文選題：行人航位推算 + 初始對(duì)準(zhǔn)　； 參考：《南昌大學(xué)》2017年碩士論文

【摘要】：隨著移動(dòng)互聯(lián)網(wǎng)快速的崛起,定位與導(dǎo)航技術(shù)被應(yīng)用在諸多領(lǐng)域。在室外空曠的環(huán)境下,利用衛(wèi)星信號(hào)的全球定位系統(tǒng)(Global Positioning System,GPS)可以較好地獲得用戶(hù)位置信息,然而在室內(nèi)環(huán)境下,衛(wèi)星信號(hào)受到阻隔很難獲得準(zhǔn)確的位置信息。目前,慣性導(dǎo)航系統(tǒng)(Inertial Navigation System,INS)依靠慣性測(cè)量單元(Inertial Measurement Unit,IMU)已成為主要的自主導(dǎo)航系統(tǒng),但是高精度的IMU體積大且價(jià)格昂貴,很難推廣使用。近些年,智能移動(dòng)設(shè)備已經(jīng)在人們生活中普及,且大部分都含有IMU等傳感器。因此,本文利用智能移動(dòng)設(shè)備的低成本傳感器,提出了一種基于多傳感器的室內(nèi)行人航位推算方法,并且針對(duì)低成本傳感器的問(wèn)題,設(shè)計(jì)了相對(duì)應(yīng)的誤差修正模型,主要分為以下三個(gè)部分:1、初始對(duì)準(zhǔn):初始對(duì)準(zhǔn)可以使INS所描述的坐標(biāo)系與導(dǎo)航坐標(biāo)系相重合,同時(shí)讓計(jì)算機(jī)在正式工作的時(shí)候有正確的初始值。由于基于智能移動(dòng)設(shè)備的IMU更易受到設(shè)備中其他元件的干擾。所以,本文研究在初始對(duì)準(zhǔn)的精對(duì)準(zhǔn)階段引入無(wú)跡卡爾曼濾波,并且融合多傳感器的數(shù)據(jù),對(duì)多傳感器誤差進(jìn)行修正,從而獲取精確的初始信息。2、運(yùn)動(dòng)狀態(tài)檢測(cè)模型:行人運(yùn)動(dòng)時(shí)通過(guò)IMU獲取正確的運(yùn)動(dòng)狀態(tài)信息對(duì)于行人航位推算方法解算高精度位置、速度和姿態(tài)信息至關(guān)重要。當(dāng)行人步伐狀態(tài)差別較大時(shí),僅依靠加速度計(jì)很難獲取正確的行人步態(tài)信息。本文研究在智能移動(dòng)設(shè)備多傳感器硬件平臺(tái)的基礎(chǔ)上,利用加速度計(jì)、陀螺儀獲取的運(yùn)動(dòng)數(shù)據(jù),設(shè)定四種閾值條件進(jìn)行步伐狀態(tài)檢測(cè)。3、多傳感器行人航位推算方法:在初始對(duì)準(zhǔn)、運(yùn)動(dòng)狀態(tài)檢測(cè)模型的基礎(chǔ)上,對(duì)于多傳感器工作時(shí)夾雜噪聲和解算時(shí)誤差累積的問(wèn)題,提出一種基于無(wú)跡卡爾曼濾波的零速度更新、零角速率更新和磁力計(jì)融合的方法,有效地對(duì)航向角以及速度誤差進(jìn)行修正。經(jīng)過(guò)多次實(shí)驗(yàn)以及數(shù)據(jù)分析,利用本文提出的方法得到的平均位置偏差占總路程的1.57%,可較好的滿(mǎn)足室內(nèi)定位需要。
[Abstract]:With the rapid rise of mobile Internet, positioning and navigation technology has been applied in many fields. In the outdoor open environment, the GPS (Global Positioning system) using satellite signal can obtain the user location information well, but in the indoor environment, it is difficult to obtain the accurate position information by blocking the satellite signal. At present, the inertial Navigation system (ins) has become the main autonomous navigation system depending on the inertial Measurement unit (IMU), but the high precision IMU is large and expensive, so it is difficult to be popularized. In recent years, smart mobile devices have become popular in people's lives, and most of them contain sensors such as IMU. Therefore, in this paper, a multisensor based indoor footpath estimation method is proposed by using the low cost sensor of intelligent mobile device, and the corresponding error correction model is designed to solve the problem of low cost sensor. The initial alignment can make the coordinate system described by INS coincide with the navigation coordinate system and at the same time make the computer have the correct initial value when it works. IMU based on intelligent mobile devices is more susceptible to interference from other components in the device. Therefore, in this paper, the unscented Kalman filter is introduced in the fine alignment phase of initial alignment, and the multi-sensor data is fused to correct the multi-sensor error. In order to obtain accurate initial information .2and the model of motion state detection: it is very important to obtain correct motion state information by means of IMU when pedestrian motion is used to calculate high precision position, velocity and attitude information of pedestrian dead-reckoning method. It is difficult to obtain correct pedestrian gait information by using accelerometers only when the pedestrian gait states are quite different. Based on the multi-sensor hardware platform of intelligent mobile devices, the motion data obtained by accelerometers and gyroscopes are studied in this paper. Four threshold conditions are set for step state detection. 3. Multi-sensor footpath estimation method: based on the initial alignment and motion state detection model, the problem of noise and error accumulation in multisensor operation is discussed. A method of zero velocity updating, zero angular rate updating and magnetometer fusion based on unscented Kalman filter is proposed, which can effectively correct the heading angle and velocity error. After many experiments and data analysis, the average position deviation obtained by the method proposed in this paper accounts for 1.57 of the total distance, which can better meet the indoor positioning needs.
【學(xué)位授予單位】：南昌大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP212;TN96;TN713

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