基于改进扩展卡尔曼滤波的姿态解算算法研究

Research on Attitude Algorithm Based on Improved Extended Calman Filter

为了解决标准扩展卡尔曼滤波器(EKF)在多旋翼无人机姿态解算中精度较低的问题,提出了一种改进扩展卡尔曼滤波算法(BPNN-EKF),使得解算精度得到较大提升。针对EKF存在预测模型参数要求具有先验已知性,在工程实践中难以获得准确的参数,以及标准EKF对非线性系统采用线性化模型带来的误差等问题,利用神经网络的非线性映射能力和自适应能力对标准EKF的估计值进行补偿,减小模型以及滤波参数误差对最优估计值的影响,从而提高最优估计精度。仿真实验证明,BPNN-EKF对多旋翼无人机姿态解算精度的提升具有显著作用。

In order to solve the problem that standard extended Kalman filter （EKF） in the multi-rotor UAV attitude solver has lower accuracy, an improved extended Kalman filter algorithm （BPNN-EKF） was proposed to improve the accuracy greatly. EKF prediction model parameters require the presence of priori known properties, but in engineering practice it is difficult to obtain accurate parameters. And nonlinear systems using linear model will cause error problem for standard EKF. Aiming at above problems, we used nonlinear mapping ability of neural network and adaptive ability to compensate the estimated value of the standard EKF, reduce the impact of the model as well as filtering parameters error for optimal estimates, thereby enhancing optimal estimation accuracy. The simulation results show that BPNN- EKF plays a significant role in improving multi-rotor UAV attitude solver accuracy.