CAJ | 학술논문

针对SINS/GPS组合导航系统噪声随时间变化引起卡尔曼滤波精度下降的问题,提出了一种噪声统计特性在线估计的自适应扩展卡尔曼滤波算法.算法首先基于新息序列实现了对观测噪声协方差的实时估计,然后基于系统方程采用协方差匹配算法完成了对过程噪声的实时跟踪.算法中尺度因子的引入进一步减小了泰勒展开造成的高阶截断误差,提高了滤波精度.仿真实验结果说明,与传统卡尔曼滤波算法相比,该算法能够实现对过程和观测噪声的完全估计,鲁棒性和精度都有明显提高.
침대SINS/GPS조합도항계통조성수시간변화인기잡이만려파정도하강적문제,제출료일충조성통계특성재선고계적자괄응확전잡이만려파산법.산법수선기우신식서렬실현료대관측조성협방차적실시고계,연후기우계통방정채용협방차필배산법완성료대과정조성적실시근종.산법중척도인자적인입진일보감소료태륵전개조성적고계절단오차,제고료려파정도.방진실험결과설명,여전통잡이만려파산법상비,해산법능구실현대과정화관측조성적완전고계,로봉성화정도도유명현제고.
To avoid the precision declination of Kalman filtering caused by the noise variation, an adaptive extended Kalman filtering is proposed to estimate noise statistical property on line in SINS/GPS integrated navigation system. First, measurement noise covariance is estimated through innovation sequence online, then the covariance matching algorithm is used to track the process noise real-time based on the system equation. Additionally, scale factor is introduced to reduce truncation error caused by Taylor formulation and thus improve estimation accuracy. The Simulations results show that, compared with the traditional Kalman filtering algorithm, the proposed algorithm is able to estimate the changes of both process and observation noise statistics simultaneous, and have higher precision and more robustness.