本文選題：捷聯(lián)式慣性導(dǎo)航 + 誤差標(biāo)定��； 參考：《哈爾濱工業(yè)大學(xué)》2017年碩士論文

【摘要】：隨著衛(wèi)星定位技術(shù)的民用化和無(wú)線互聯(lián)網(wǎng)技術(shù)的快速發(fā)展,定位導(dǎo)航應(yīng)用已經(jīng)融入到日常生活的方方面面。由于人們對(duì)室內(nèi)定位需求的不斷增加,如在商場(chǎng)、展會(huì)等室內(nèi)場(chǎng)所中通常需要了解人員的位置信息。室內(nèi)無(wú)線定位技術(shù)需要在室內(nèi)環(huán)境中布設(shè)大量的無(wú)線信號(hào)發(fā)射器,價(jià)格不菲且定位精度有待提高。慣性導(dǎo)航技術(shù)僅依靠自身傳感器實(shí)現(xiàn)自主導(dǎo)航為行人導(dǎo)航提供了技術(shù)參考。目前已有相應(yīng)的產(chǎn)品應(yīng)用到特殊的領(lǐng)域中,如消防員在室內(nèi)火災(zāi)現(xiàn)場(chǎng)的定位和礦井下工人的定位等,但定位設(shè)備的慣性傳感器精度較高且造價(jià)成本高,不適用于消費(fèi)電子產(chǎn)品。因此,本文基于低成本的慣性傳感器對(duì)行人導(dǎo)航技術(shù)展開(kāi)研究。論文首先對(duì)捷聯(lián)式慣性導(dǎo)航理論進(jìn)行研究,探討適合用于行人導(dǎo)航的更新算法。導(dǎo)航姿態(tài)矩陣計(jì)算是捷聯(lián)慣性導(dǎo)航系統(tǒng)中關(guān)鍵算法,構(gòu)建載體坐標(biāo)系到導(dǎo)航坐標(biāo)系的變換關(guān)系模型,用四元數(shù)法建立姿態(tài)更新的微分方程,并通過(guò)龍格-庫(kù)塔法求解方程。再將測(cè)量加速度通過(guò)姿態(tài)矩陣轉(zhuǎn)換到導(dǎo)航坐標(biāo)系下,利用積分算法求得速度和位置等導(dǎo)航參數(shù)。由于低成本的MEMS三軸矢量場(chǎng)傳感器(陀螺儀、加速度計(jì)和磁力計(jì))存在較嚴(yán)重的輸出誤差,本文針對(duì)傳感器誤差的標(biāo)定與補(bǔ)償方法進(jìn)行探討與研究。三軸矢量場(chǎng)傳感器由于制造工藝的差異存在零偏、標(biāo)度因數(shù)與不正交角誤差。本文對(duì)傳感器誤差建立數(shù)學(xué)模型,采用最小二乘橢球擬合的方法來(lái)確定誤差參數(shù)大小。通過(guò)實(shí)驗(yàn)驗(yàn)證該方法能有效估算傳感器的主要誤差參數(shù),經(jīng)過(guò)誤差補(bǔ)償后可改善導(dǎo)航系統(tǒng)的精度。由于傳感器隨機(jī)漂移誤差難以在使用前進(jìn)行標(biāo)定,需結(jié)合濾波器以減小誤差的累積�；パa(bǔ)濾波器結(jié)構(gòu)簡(jiǎn)單、計(jì)算量小,與梯度下降法結(jié)合能使誤差快速收斂;擴(kuò)展卡爾曼濾波通過(guò)建立狀態(tài)方程和量測(cè)方程能有效估計(jì)陀螺儀的隨機(jī)漂移量。本文提出根據(jù)觀測(cè)量可信時(shí)刻建立相應(yīng)的量測(cè)方程,能有效提高估計(jì)精度。經(jīng)過(guò)濾波器對(duì)慣性傳感器數(shù)據(jù)融合后仍存在較嚴(yán)重的速度漂移,根據(jù)行人行走的步態(tài)特征通過(guò)零速判斷和修正的方法校正。最后,本文對(duì)這兩種濾波算法進(jìn)行對(duì)比實(shí)驗(yàn),以分析其應(yīng)用于行人定位導(dǎo)航的精確度情況。第一項(xiàng)實(shí)驗(yàn)在旋轉(zhuǎn)平臺(tái)上進(jìn)行,轉(zhuǎn)臺(tái)能提供精確的旋轉(zhuǎn)角度信息,通過(guò)導(dǎo)航系統(tǒng)輸出的姿態(tài)信息與轉(zhuǎn)臺(tái)給定角度作比較,分析傳感器數(shù)據(jù)融合的實(shí)時(shí)性和跟隨精度。第二項(xiàng)實(shí)驗(yàn)是在室內(nèi)環(huán)境中檢驗(yàn)行人導(dǎo)航系統(tǒng)的定位軌跡并分析誤差。兩項(xiàng)實(shí)驗(yàn)結(jié)果表明本文設(shè)計(jì)的傳感器數(shù)據(jù)融合算法能很好滿足行人導(dǎo)航的需求。
[Abstract]:With the civilian use of satellite positioning technology and the rapid development of wireless Internet technology, positioning and navigation applications have been integrated into every aspect of daily life. Due to the increasing demand for indoor positioning, people usually need to know the location information in the shopping mall, exhibition and other indoor places. Indoor wireless location technology needs to set up a large number of wireless signal transmitters in indoor environment, which is expensive and needs to be improved. Inertial navigation technology only rely on its own sensors to achieve autonomous navigation for pedestrian navigation provides a technical reference. At present, the corresponding products have been applied to special fields, such as firemen's location in indoor fire scene and mine workers' positioning, but the inertial sensor of positioning equipment has high precision and high cost, so it is not suitable for consumer electronic products. Therefore, based on the low-cost inertial sensor, pedestrian navigation technology is studied in this paper. In this paper, the theory of strapdown inertial navigation is studied, and the updating algorithm for pedestrian navigation is discussed. The calculation of navigation attitude matrix is a key algorithm in strapdown inertial navigation system. The transformation relationship model from carrier coordinate system to navigation coordinate system is constructed, and the differential equation of attitude updating is established by quaternion method, and the equation is solved by Runge-Kutta method. Then the acceleration measurement is transformed into the navigation coordinate system by attitude matrix, and the navigation parameters such as velocity and position are obtained by integral algorithm. Due to the serious output errors of MEMS three-axis vector field sensors (gyroscopes accelerometers and magnetometers) with low cost the calibration and compensation methods of sensor errors are discussed and studied in this paper. Three axis vector field sensor has zero deviation and scale factor error due to the difference of manufacturing process. In this paper, the mathematical model of sensor error is established, and the error parameter is determined by least square ellipsoid fitting method. The experimental results show that the method can effectively estimate the main error parameters of the sensor and improve the accuracy of the navigation system after error compensation. Because the random drift error of the sensor is difficult to calibrate before use, it is necessary to combine the filter to reduce the accumulation of errors. The complementary filter has the advantages of simple structure and small computational complexity, and the extended Kalman filter can effectively estimate the random drift of gyroscope by establishing the state equation and measuring equation, and combining with gradient descent method can make the error converge rapidly. In this paper, it is proposed that the estimation accuracy can be improved effectively by establishing the corresponding measurement equation according to the credible time of the observation. After the data fusion of the inertial sensor by the filter, there is still a serious velocity drift, which is corrected by the method of zero speed judgment and correction according to the gait characteristics of the pedestrian. Finally, this paper compares the two filtering algorithms to analyze the accuracy of their application in pedestrian navigation. The first experiment is carried out on the rotating platform. The turntable can provide accurate rotation angle information. The real-time and follow accuracy of sensor data fusion are analyzed by comparing the attitude information output by the navigation system with the given angle of the turntable. The second experiment is to test the location track of pedestrian navigation system and analyze the error in indoor environment. Two experimental results show that the sensor data fusion algorithm designed in this paper can well meet the needs of pedestrian navigation.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP212.9

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