基于四元數(shù)和卡爾曼濾波的姿態(tài)角估計算法研究與應(yīng)用
發(fā)布時間：2018-08-31 09:15
【摘要】：姿態(tài)估計最早應(yīng)用于軍事航天領(lǐng)域,并在該領(lǐng)域的研究比較成熟,但由于傳統(tǒng)慣性傳感器(陀螺儀和加速度計)體積較大、成本高、攜帶不便等原因影響了姿態(tài)估計在其他領(lǐng)域的應(yīng)用。近年來,隨著MEMS(Micro Electro Mechanical System,微機械電子系統(tǒng))技術(shù)的發(fā)展,特別是MEMS慣性傳感器和MEMS磁強計的出現(xiàn),擴展了姿態(tài)估計在其他領(lǐng)域的應(yīng)用,例如在醫(yī)學(xué)領(lǐng)域中的內(nèi)窺鏡姿態(tài)定位技術(shù),虛擬與現(xiàn)實領(lǐng)域中的情景交互、手勢識別等。姿態(tài)指的是一個坐標(biāo)系和另外一個坐標(biāo)系之間的位置關(guān)系,常用一組姿態(tài)角:俯仰角、橫滾角和偏航角來描述。里面涉及到姿態(tài)角的求解,即姿態(tài)解算問題和姿態(tài)角的精度問題,本文應(yīng)用姿態(tài)角檢測跌倒事件的發(fā)生,就是以姿態(tài)角計算的準(zhǔn)確性為前提。描述姿態(tài)的方法有很多,包括歐拉角法、方向余弦法和四元數(shù)法等。歐拉角描述姿態(tài)會出現(xiàn)奇異性問題,方向余弦法計算量較大,目前廣泛采用四元數(shù)法,本文基于四元數(shù)法,利用四階龍格庫塔法求解四元數(shù)微分方程得到姿態(tài)角,實現(xiàn)姿態(tài)解算。由于MEMS陀螺儀自身的特性,靜態(tài)情況下其輸出的信號中包含常值誤差和隨機漂移誤差,這就會造成在采用陀螺儀計算角度時會產(chǎn)生角度漂移,無法實現(xiàn)長時間的精確測量;同樣,加速度計根據(jù)重力場計算出水平傾角(俯仰角和橫滾角),而實際應(yīng)用中載體都是運動的,勢必會引入線性加速度,造成水平傾角計算的不準(zhǔn)確,磁強計根據(jù)地球磁場計算出偏航角,但由于地磁場容易受到干擾,那么計算的偏航角也不會太準(zhǔn)確。為解決以上問題,目前廣泛采用多傳感器信息融合的方式進行姿態(tài)角估計,本文在參考前人融合算法的基礎(chǔ)上,提出了自己的融合方案:采用卡爾曼濾波算法對三軸陀螺儀、三軸加速度計和三軸磁強計進行信息融合,得到最優(yōu)估計角度。首先,根據(jù)傳感器輸出的數(shù)據(jù)建立隨機漂移AR誤差模型,對原始數(shù)據(jù)進誤差補償和濾波,然后將陀螺儀計算的角度作為估計角度,將加速度計和磁強計計算的角度作為測量角度,利用卡爾曼濾波算法融合估計角度和測量角度,得到最優(yōu)的姿態(tài)角估計。并對提出的融合算法進行實際驗證,達到預(yù)期效果。最后,按照設(shè)計的算法,利用九軸傳感器MPU-9150和Arduino Pro mini設(shè)計了可佩戴于腰部的姿態(tài)角檢測裝置,實現(xiàn)對人體跌倒姿態(tài)的檢測,通過實際測試證明了其可靠性并提出了改進的意見。本文作為一篇工程應(yīng)用文,將姿態(tài)角估計應(yīng)用于人體跌倒姿態(tài)的檢測中,為姿態(tài)估計在其他領(lǐng)域的應(yīng)用起到了推動作用,有一定的應(yīng)用價值和實際意義。
[Abstract]:Attitude estimation was first applied in the military space field, and the research in this field is mature. However, the traditional inertial sensors (gyroscopes and accelerometers) are large in size and high in cost. The inconvenience of carrying affects the application of attitude estimation in other fields. In recent years, with the development of MEMS (Micro Electro Mechanical System, technology, especially the emergence of MEMS inertial sensors and MEMS magnetometers, the application of attitude estimation in other fields, such as endoscope attitude positioning technology in medical field, has been expanded. Scene interaction, gesture recognition and so on in the field of virtual reality. Attitude refers to the position relationship between one coordinate system and another coordinate system, which is usually described by a set of attitude angles: pitch angle, roll angle and yaw angle. In this paper, attitude angle is used to detect the fall event, which is based on the accuracy of attitude angle calculation. There are many methods to describe attitude, including Euler angle method, directional cosine method and quaternion method. The singularity problem occurs in Euler angle describing attitude, and the direction cosine method has a large amount of calculation. At present, the quaternion method is widely used. Based on the quaternion method, the attitude angle is obtained by solving the quaternion differential equation with the fourth order Runge-Kutta method. The attitude calculation is realized. Because of the characteristic of MEMS gyroscope, the output signal of MEMS gyroscope contains constant error and random drift error in the static condition, which will result in angle drift when using gyroscope to calculate the angle, which can not realize the accurate measurement for a long time. In the same way, the accelerometer calculates the horizontal inclination angle (pitch angle and roll angle) according to the gravity field. However, in practical application, the carrier is moving, which will inevitably introduce linear acceleration, resulting in the inaccuracy of the calculation of the horizontal inclination angle. The yaw angle of the magnetometer is calculated according to the earth's magnetic field, but because the geomagnetic field is easily disturbed, the calculated yaw angle is not too accurate. In order to solve the above problems, multi-sensor information fusion is widely used to estimate the attitude angle. In this paper, based on the previous fusion algorithms, our own fusion scheme is proposed: the Kalman filter algorithm is applied to the three-axis gyroscope. The three-axis accelerometer and three-axis magnetometer are fused to obtain the optimal angle estimation. Firstly, the random drift AR error model is established according to the output data of the sensor, and the error compensation and filtering of the original data are made. Then, the angle of the gyroscope calculation is used as the estimation angle. The angle calculated by accelerometer and magnetometer is taken as the measuring angle, and the optimal attitude angle estimation is obtained by combining the estimation angle with the measurement angle using Kalman filter algorithm. The proposed fusion algorithm is verified to achieve the desired results. Finally, according to the designed algorithm, the attitude angle detection device which can be worn on the waist is designed by using MPU-9150 and Arduino Pro mini, which can be worn in the waist. The reliability of the device is proved by the actual test and the improved advice is put forward. In this paper, as an engineering application, attitude angle estimation is applied to the detection of human fall attitude, which plays an important role in the application of attitude estimation in other fields, and has certain application value and practical significance.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713;TP212
本文編號：2214556
[Abstract]:Attitude estimation was first applied in the military space field, and the research in this field is mature. However, the traditional inertial sensors (gyroscopes and accelerometers) are large in size and high in cost. The inconvenience of carrying affects the application of attitude estimation in other fields. In recent years, with the development of MEMS (Micro Electro Mechanical System, technology, especially the emergence of MEMS inertial sensors and MEMS magnetometers, the application of attitude estimation in other fields, such as endoscope attitude positioning technology in medical field, has been expanded. Scene interaction, gesture recognition and so on in the field of virtual reality. Attitude refers to the position relationship between one coordinate system and another coordinate system, which is usually described by a set of attitude angles: pitch angle, roll angle and yaw angle. In this paper, attitude angle is used to detect the fall event, which is based on the accuracy of attitude angle calculation. There are many methods to describe attitude, including Euler angle method, directional cosine method and quaternion method. The singularity problem occurs in Euler angle describing attitude, and the direction cosine method has a large amount of calculation. At present, the quaternion method is widely used. Based on the quaternion method, the attitude angle is obtained by solving the quaternion differential equation with the fourth order Runge-Kutta method. The attitude calculation is realized. Because of the characteristic of MEMS gyroscope, the output signal of MEMS gyroscope contains constant error and random drift error in the static condition, which will result in angle drift when using gyroscope to calculate the angle, which can not realize the accurate measurement for a long time. In the same way, the accelerometer calculates the horizontal inclination angle (pitch angle and roll angle) according to the gravity field. However, in practical application, the carrier is moving, which will inevitably introduce linear acceleration, resulting in the inaccuracy of the calculation of the horizontal inclination angle. The yaw angle of the magnetometer is calculated according to the earth's magnetic field, but because the geomagnetic field is easily disturbed, the calculated yaw angle is not too accurate. In order to solve the above problems, multi-sensor information fusion is widely used to estimate the attitude angle. In this paper, based on the previous fusion algorithms, our own fusion scheme is proposed: the Kalman filter algorithm is applied to the three-axis gyroscope. The three-axis accelerometer and three-axis magnetometer are fused to obtain the optimal angle estimation. Firstly, the random drift AR error model is established according to the output data of the sensor, and the error compensation and filtering of the original data are made. Then, the angle of the gyroscope calculation is used as the estimation angle. The angle calculated by accelerometer and magnetometer is taken as the measuring angle, and the optimal attitude angle estimation is obtained by combining the estimation angle with the measurement angle using Kalman filter algorithm. The proposed fusion algorithm is verified to achieve the desired results. Finally, according to the designed algorithm, the attitude angle detection device which can be worn on the waist is designed by using MPU-9150 and Arduino Pro mini, which can be worn in the waist. The reliability of the device is proved by the actual test and the improved advice is put forward. In this paper, as an engineering application, attitude angle estimation is applied to the detection of human fall attitude, which plays an important role in the application of attitude estimation in other fields, and has certain application value and practical significance.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TN713;TP212
【參考文獻】
相關(guān)期刊論文 前6條
1 金光明;張國良;陳林鵬;田琦;;MEMS陀螺儀靜態(tài)漂移模型與濾波方法研究[J];傳感器與微系統(tǒng);2007年11期
2 王森;陳曉冬;杜承陽;汪毅;郁道銀;;磁干擾環(huán)境下基于多傳感器的內(nèi)窺鏡姿態(tài)定位技術(shù)[J];傳感技術(shù)學(xué)報;2013年08期
3 呂印新;肖前貴;胡壽松;;基于四元數(shù)互補濾波的無人機姿態(tài)解算[J];燕山大學(xué)學(xué)報;2014年02期
4 臧榮春,崔平遠;陀螺隨機漂移時間序列建模方法研究[J];系統(tǒng)仿真學(xué)報;2005年08期
5 田蔚風(fēng),金志華,陸愷;神經(jīng)網(wǎng)絡(luò)在陀螺漂移誤差模型辨識中的應(yīng)用[J];中國慣性技術(shù)學(xué)報;1998年03期
6 劉魯源,陳玉柱,陳剛,李士心;基于小波變換的陀螺漂移建模與實驗研究[J];中國慣性技術(shù)學(xué)報;2004年01期
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