A dynamic adaptive AHRS algorithm for UAV based on SVDCKF

一种基于SVDCKF的无人机动态自适应航姿算法

School of Automation, Northwestern Polytechnical University, Xi'an 710129, China

Received: 29 June 2020

Abstract

Aiming at the attitude solution accuracy and robustness for small UAVs in complex flight conditions, this paper proposes a dynamic adaptive attitude and heading systems(AHRS) estimator with singular value decomposition Cubature Kalman filter(SVDCKF). Considering the problem of random bias for the low-cost attitude sensor, this paper designs a method that the sensor random bias is used as the state vector to eliminate the effect of the sensor random bias. Due to the non-linearity of small UAVs AHRS model and the non-positive definite phenomenon of the covariance matrix, a nonlinear AHRS filter combined with the Cubature Kalman filter and singular value decomposition is designed to improve the attitude solution accuracy. In addition, when the UAV flies in the different flight conditions, the three-axis acceleration of the attitude sensor will affect the attitude solution. Thus, a dynamic adaptive factor based on adaptive filtering is used to adjust continuously the acceleration noise variance to improve the robustness of the AHRS. The experimental results show that the method and algorithm proposed not only improve the attitude solution accuracy, and satisfy the flight requirements of small UAVs, but also eliminate the influence of the attitude sensor random bias and three-axis acceleration for the attitude solution to improve the proposed algorithm robustness and anti-interference.

摘要

针对小型无人机在复杂飞行条件下的航姿解算精度和鲁棒性问题，提出了一种动态自适应调节的奇异值容积卡尔曼滤波航姿估计算法。考虑到低成本航姿传感器随机偏差大的问题，将航姿传感器随机偏差作为待估计参数，以消除传感器随机偏差的影响。由于无人机航姿模型的非线性和滤波中协方差矩阵的非正定问题，设计了一种融合容积卡尔曼滤波（cubature Kalman filter，CKF）和奇异值分解（singular value decomposition，SVD）的非线性航姿滤波器来改善航姿解算精度。另外考虑到不同的飞行条件下，航姿传感器中三轴加速度对无人机航姿解算的影响，基于自适应滤波的思想，提出了一种动态自适应因子来不断地调节加速度测量噪声方差，提高了航姿滤波在复杂条件下的鲁棒性。实验结果表明，所提算法不仅有效地改善了非线性航姿模型的航姿解算精度，满足小型无人机的飞行需求，而且消除了航姿传感器随机偏差和三轴加速度测量噪声对航姿解算的影响，提高了算法的鲁棒性和抗扰性。