改进的自适应最大相关熵UKF算法在SINS/GNSS组合导航中的应用

Application of Improved Adaptive Maximum Correntropy UKF Algorithm in SINS/GNSS Integrated Navigation

组合导航系统是典型的非线性系统,模型的非线性以及噪声的不确定性均会影响系统的估计精度。针对传统无迹卡尔曼滤波器在非高斯噪声情况下滤波精度下降的问题,提出了一种改进的自适应最大相关熵无迹卡尔曼滤波(UKF)算法。该方法依据滤波新息对当前时刻量测噪声进行估计,利用估计噪声相对于历史噪声的变化程度确定核宽的变化区间,并依据最大相关熵算法的迭代误差变化对核宽进行自适应调整,提高了算法的收敛速度以及对非高斯噪声的处理能力。基于非高斯噪声环境进行了SINS/GNSS组合导航仿真实验与实际跑车实验,与传统无迹卡尔曼滤波进行比较,结果表明:在非高斯噪声条件下,提出的算法得到的东向位置误差为2.11 m,北向位置误差为1.85 m,滤波性能明显优于传统UKF,提升了组合导航解算的精度。

The integrated navigation system is a typical nonlinear system.The model nonlinearity and noise uncertainty will affect the estimation accuracy of the system.Aiming at the problem that the filtering accuracy of traditional unscented Kalman filter decreases in the case of non-Gaussian noise,an improved adaptive maximum correntropy unscented Kalman filter algorithm is proposed.This method estimates the measurement noise at the current moment according to the filtering innovation,and determines the change interval of the kernel width by using the change degree of the estimated noise relative to the historical noise.The kernel width is adaptively adjusted according to the iterative error change of the maximum correntropy algorithm,which improves the convergence speed of the algorithm and the processing ability of non-Gaussian noise.Based on the non-Gaussian noise environment,the SINS/GNSS integrated navigation simulation experiment is built and the actual car experiment is carried out.Compared with the traditional unscented Kalman filter,the results show that the east position error and north position error obtained by the proposed algorithm are 2.11 m and 1.85 m under the condition of non-Gaussian noise.The filtering performance is obviously better than the traditional UKF,which improves the accuracy of the integrated navigation solution.