一种自适应混合滤波姿态解算算法

针对低成本惯性测量器件采用基于滤波的姿态解算算法存在精度低、抗干扰性差等问题，提出了一种基于共轭梯度法与互补滤波相融合的自适应参数调节的混合滤波算法.该算法首先利用共轭梯度算法对加速度计和磁力计的数据进行姿态四元数的迭代估算，再通过互补滤波算法将陀螺仪更新的姿态与其进行信息融合，最后根据载体的运动状态自适应调节滤波参数，实现最优姿态估计.为验证所提算法的可行性和抗干扰性，与其他滤波融合算法在移动机器人平台上进行抗磁干扰和抗运动加速度干扰实验.实验结果表明，该算法可以有效地降低磁干扰和运动加速度干扰对姿态角解算的影响，其姿态角解算精度优于传统的梯度下降法、高斯牛顿法和共轭梯度法的滤波融合算法.

An Adaptive Hybrid Filter Algorithm for Attitude Estimation

In order to solve the problems such as low precision, poor anti-jamming ability in attitude estimation algorithms based on filtering for low-cost inertial sensors, a new adaptive hybrid filter algorithm based on conjugate gradient method and complementary filter is proposed. The conjugate gradient algorithm adopted to process the data measured by accelerometer and magnetometer at first, and estimated their attitude quaternion iteratively. Then, the complementary filter is used to fuse the information of the gyro updated attitude and the iterative optimized attitude of the conjugate gradient method. Finally, according to the motion state of the carrier, the complementary filtering parameter is adjusted adaptively to obtain the optimal attitude estimation. To verify that the algorithm is feasible and anti-interference, it is compared with other filter fusion algorithms in the experiments of anti-magnetic interference and anti-interference of motion acceleration. It is shown that the proposed algorithm effectively reduces the interferences caused by magnetic field and motion acceleration, and has better precision of attitude angle estimation than that of traditional gradient descent algorithm, Gauss Newton algorithm and conjugate gradient algorithm.