CHEN Zhuo, REN Jiu-chun, ZHU Qian. Adaptive attitude fusion algorithm based on gradient descent[J]. 2019, (3): 124-126. DOI: 10.13873/J.1000-9787(2019)03-0124-03.
基于梯度下降的自适应姿态融合算法
摘要
针对第一代帆船姿态测量系统中采用的自适应卡尔曼滤波算法作用范围有限、动态性能较差等缺陷
基于微机电系统(MEMS)惯性传感器与梯度下降姿态融合算法提出了两种自适应方法
分别根据当前时刻之前N个采样点的平均运动加速度与加速度的变化量设计自适应控制因子
得到稳定的动态梯度下降步长。实验结果表明:两种算法性能均优于自适应卡尔曼滤波与单点加速度抑制法
其中
基于加速度增量的控制算法更加符合高速运动状态下加速度剧烈变化的实际规律
测量性能达到最优
符合海面帆船运动船体姿态测量的实际需求。
Abstract
Aiming at defect of the action range of the adaptive Kalman filtering algorithm used in the first generation of the sailing attitudes
measurement system is limited and dynamic performance is poor
two adaptive methods are presented based on micro-electro-mechanical system( MEMS) inertial sensors and gradient descent attitude fusion algorithm. Adptive control factors are designed according to the average motion acceleration and the variation of acceleration of N sampling points before the current time respectively to obtain stable dyamic gradient descent steps. Experimental results indicate that the performances of the two algorithms are prior to the adaptive Kalman filtering and single-point acceleration restrained algorithms
among them
the acceleration increment-based control algorithm is proved to be consistent with real rule of drastic acceleration changes in high-speed motion state
measurement performance achieve the optimum which meets the real requirement of sailing attitude measurement.