慣性測(cè)量單元姿態(tài)融合的動(dòng)態(tài)分析
信息技術(shù)與網(wǎng)絡(luò)安全
謝 敏1,2,趙來(lái)定1,2,王召文1,2 )
(1.南京郵電大學(xué) 通信與信息工程學(xué)院,江蘇 南京210003; 2.南京郵電大學(xué) 通信與網(wǎng)絡(luò)技術(shù)國(guó)家地方聯(lián)合工程研究中心,江蘇 南京210003
摘要: 針對(duì)慣性測(cè)量單元背景噪聲和器件漂移等問(wèn)題,提出了一種基于梯度下降法、互補(bǔ)濾波和卡爾曼濾波的融合算法。首先,利用加速度計(jì)和磁力計(jì)的數(shù)據(jù)通過(guò)幾何計(jì)算得出姿態(tài)角。其次,利用梯度下降法,將加速度計(jì)和陀螺儀的數(shù)據(jù)進(jìn)行基于四元數(shù)法的數(shù)據(jù)融合,得出姿態(tài)角。接著,利用互補(bǔ)濾波法將姿態(tài)數(shù)據(jù)再次結(jié)合。最后,利用卡爾曼算法對(duì)結(jié)合后的數(shù)據(jù)進(jìn)行濾波處理。通過(guò)動(dòng)態(tài)實(shí)驗(yàn)表明,經(jīng)過(guò)此融合算法的慣性測(cè)量單元與傳統(tǒng)算法相比,數(shù)據(jù)輸出更加準(zhǔn)確,連續(xù)性提高,并擁有很好的平穩(wěn)特性。
中圖分類號(hào): TP202
文獻(xiàn)標(biāo)識(shí)碼: A
DOI: 10.19358/j.issn.2096-5133.2021.07.016
引用格式: 謝敏,趙來(lái)定,王召文. 慣性測(cè)量單元姿態(tài)融合的動(dòng)態(tài)分析[J].信息技術(shù)與網(wǎng)絡(luò)安全,2021,40(7):95-102.
文獻(xiàn)標(biāo)識(shí)碼: A
DOI: 10.19358/j.issn.2096-5133.2021.07.016
引用格式: 謝敏,趙來(lái)定,王召文. 慣性測(cè)量單元姿態(tài)融合的動(dòng)態(tài)分析[J].信息技術(shù)與網(wǎng)絡(luò)安全,2021,40(7):95-102.
Dynamic analysis of inertial measurement unit attitude fusion
Xie Min1,2,Zhao Laiding1,2,Wang Zhaowen1,2
(1.College of Telecommunications and Information Engineering,Nanjing University of Posts and Telecommunications, Nanjing 210003,China; 2.National Local Joint Enginnering Research Center for Communication and Network Technology, Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
Abstract: In order to solve the problems of inertial measurement unit background noise and device drift, this paper proposes a fusion algorithm based on gradient descent, complementary filtering and Kalman filtering. Firstly, the attitude angle is calculated through geometric calculations using the accelerometer and magnetometer data. Secondly, using the gradient descent method, the accelerometer and gyroscope data are fused based on the quaternion method to calculate the attitude angles. Then, the data is merged again using the complementary filtering method to obtain the attitude data. Finally, Kalman algorithm is used to filter the combined data. The dynamic experiments show that the data output of inertial measurement unit based on this fusion algorithm is more accurate, the continuity is improved, and it has good stability compared with the traditional algorithm.
Key words : inertial measurement unit;gradient descent method;complementary filtering;Kalman filtering;attitude fusion
0 引言
微機(jī)電系統(tǒng)內(nèi)部集成了動(dòng)態(tài)傳感器、數(shù)字信號(hào)處理模塊、串口通信等模塊,是信息技術(shù)和機(jī)械工程的結(jié)合[1]。目前,微機(jī)電傳感器具有成本低、體積小、功耗低、可靠性高等優(yōu)勢(shì),廣泛用于高智能化的行業(yè)中。因此,慣性測(cè)量單元(Inertial Measurement Unit,IMU)常采用微機(jī)電傳感器。
慣性測(cè)量單元應(yīng)用廣泛,遠(yuǎn)至國(guó)家軍事防御,近至日常智能設(shè)備。慣性測(cè)量單元可以測(cè)量裝置或載體自身的加速度、角度等狀態(tài)數(shù)據(jù),一般通過(guò)加速度計(jì)等傳感器進(jìn)行測(cè)量。其中,陀螺儀用于測(cè)量設(shè)備自身的旋轉(zhuǎn)運(yùn)動(dòng)。加速度計(jì)用于測(cè)量裝置或載體的受力情況[2]。磁力計(jì)用于確定設(shè)備所處的方位。
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作者信息:
謝 敏1,2,趙來(lái)定1,2,王召文1,2
(1.南京郵電大學(xué) 通信與信息工程學(xué)院,江蘇 南京210003;
2.南京郵電大學(xué) 通信與網(wǎng)絡(luò)技術(shù)國(guó)家地方聯(lián)合工程研究中心,江蘇 南京210003)
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