本文關(guān)鍵詞： 傳感器 信息融合 高斯牛頓算法 互補(bǔ)濾波 人體運動捕獲　出處：《工程科學(xué)與技術(shù)》2017年05期 　論文類型：期刊論文

【摘要】：針對基于慣性傳感器的人體運動捕獲系統(tǒng)存在陀螺漂移和噪聲干擾等問題,提出一種多元傳感器信息融合的自適應(yīng)混合濾波融合算法。算法首先利用快速高斯牛頓法對加速度計和磁力計數(shù)據(jù)進(jìn)行姿態(tài)信息迭代估算,用四元數(shù)將參考坐標(biāo)系中的加速度和磁場強(qiáng)度分量轉(zhuǎn)換到載體坐標(biāo)中,將轉(zhuǎn)換后的值與當(dāng)前時刻測量值的差值代入高斯牛頓迭代算法中用于四元數(shù)的實時值估計,通過確定搜索步長的最優(yōu)值來縮短迭代次數(shù),提高算法收斂速度。設(shè)計自適應(yīng)的互補(bǔ)濾波器將高斯牛頓法解算的姿態(tài)信息作為觀測矢量對陀螺漂移進(jìn)行補(bǔ)償,分別使用高通濾波器和低通濾波器處理陀螺儀數(shù)據(jù)和高斯牛頓算法優(yōu)化過后的加速度計、磁力計數(shù)據(jù)。在互補(bǔ)濾波器中引入重力矢量及地磁參考矢量自適應(yīng)調(diào)節(jié)濾波器參數(shù)用于實時調(diào)整不同算法的權(quán)重大小,融合后輸出最終的姿態(tài)信息,實現(xiàn)最優(yōu)估計。進(jìn)行實驗對比分析本算法和其他算法融合效果,結(jié)果表明,本算法有效降低陀螺累積誤差、線性加速度及磁場對解算精度的干擾,磁干擾狀態(tài)下誤差為0.94°,自由運動狀態(tài)下誤差為1°。對比擴(kuò)展卡爾曼濾波融合算法,本文算法執(zhí)行時間縮短25%,有效提升了運動捕獲系統(tǒng)的性能。
[Abstract]:Aiming at the problems of gyro drift and noise interference in the human motion capture system based on inertial sensor, An adaptive hybrid filtering fusion algorithm for multi-sensor information fusion is proposed. Firstly, the fast Gao Si Newton method is used to estimate the attitude information of accelerometers and magnetometers. The acceleration and magnetic field intensity components in the reference coordinate system are converted to the carrier coordinates by quaternions. The difference between the converted values and the measured values at the current time is substituted in the Gao Si Newton iterative algorithm for the real-time estimation of quaternions. By determining the optimal value of the search step size, the iteration times are shortened and the convergence rate of the algorithm is improved. An adaptive complementary filter is designed to compensate the gyro drift with the attitude information calculated by Gao Si Newton method as the observation vector. Using high-pass filter and low-pass filter respectively to process gyroscope data and the accelerometer optimized by Gao Si Newton algorithm, The parameters of gravity vector and geomagnetic reference vector are introduced into the complementary filter to adjust the weights of different algorithms in real time, and the final attitude information is output after fusion. The experimental results show that the proposed algorithm can effectively reduce the error of gyroscope accumulation, the interference of linear acceleration and magnetic field on the accuracy of the solution. The error is 0.94 擄in the state of magnetic interference and 1 擄in the state of free motion. Compared with the extended Kalman filter fusion algorithm, the execution time of this algorithm is shortened by 25%, which effectively improves the performance of the motion capture system.
【作者單位】： 重慶郵電大學(xué)光電信息感測與傳輸技術(shù)重慶市重點實驗室;
【基金】：國家自然科學(xué)基金資助項目(51175535) 國際聯(lián)合研究中心科技平臺與基地建設(shè)項目資助(cstc2014gjhz0038) 重慶市基礎(chǔ)與前沿研究計劃資助項目(cstc2015jcyj BX0068) 重慶郵電大學(xué)博士啟動基金資助項目(A2015-40);重慶郵電大學(xué)自然科學(xué)基金資助項目(A2015-49)
【分類號】：TN713;TP212
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本文編號：1515364