基于变步长动量梯度下降法的姿态解算算法

An Attitude Algorithm Based on Variable-Step-Size Momentum Gradient Descent Method

针对基于MEMS的姿态测量系统因为存在误差而导致姿态解算漂移的问题,提出一种基于变步长动量梯度下降的姿态解算算法。该算法从传感器数据去噪和姿态解算两部分提高姿态解算精度。在传感器数据去噪方面,为了工程实用,降低微处理器计算量,提出一种改进递推限幅均值滤波算法;在姿态解算方面,在梯度下降法的基础上设计自适应步长,使用动量梯度优化每次迭代方向,使得每次迭代后误差最小,同时使用动态限幅滤波抑制角度振荡。静态和动态实验结果均表明了所提算法的有效性和优越性。

Aiming at the problem of attitude calculation drift caused by the error of the MEMS based attitude measurement system,an attitude algorithm based on variable-step-size momentum gradient descent is proposed.The algorithm improves the attitude calculation accuracy from two aspects of sensor data denoising and attitude calculating.In sensor data denoising,an improved recursive amplitude-limited mean filtering algorithm is proposed to reduce the computational complexity of microprocessors.In attitude calculating,an adaptive step size is designed based on gradient descent method,and momentum gradient is used to optimize the direction of each iteration so as to minimize the error after each iteration,and dynamic limiting filter is used to suppress angle oscillation.Results of the static and dynamic experiments show the effectiveness and superiority of the proposed algorithm.