一种基于MAP噪声估计器的低成本SINS/GPS的快速UKF算法

A Fast UKF Algorithm Based on MAP Noise Estimator for Low Cost SINS/GPS System

为进一步提高平方根无迹卡尔曼滤波(SR-UKF)在低成本捷联惯导(SINS)/卫星导航(GPS)组合系统中的算法精度和实时性,针对系统随机模型的噪声统计特征不确定性和协方差平方根矩阵的复杂性,结合最大后验估计(MAP)和矩阵奇异值分解(SVD)理论建模,提出了一种基于MAP噪声估计模型的快速UKF算法.通过对SINS/GPS组合导航系统的仿真实验,研究表明:相比于传统UKF算法,新算法能够有效减小噪声统计模型不确定时对导航精度的制约,提高算法鲁棒性,同时降低传统UKF算法的时间复杂度,提高数据更新实时性.

To further improve the performance of unscented Kalman filter （UKF） algorithm in SINS/GPS navigation system, an improved UKF filtering based on the MAP and SVD algorithm is discussed in this paper. The new algorithm is solved the statistical noise characteristics of the system stochastic model uncertainty and complexity of the square root of the covariance matrix. The simulation experiment results of SINS / GPS integrated navigation system shows that compared with the traditional UKF algorithm, the new algorithm implement in SINS/GPS integrated navigation system could improve navigation accuracy, increase system robustness, and speed up the frequency of update data.