扩展卡尔曼滤波技术可以利用序列观测重力异常数据以及航行区域重力异常基准图来校正惯性导航系统漂移误差。针对因重力测量环境的变化、测量仪器扰动等因素造成的重力异常观测噪声不确定问题,提出了基于量测残差自适应估计观测噪声协方差(residual-based adaptive estimation,RAE)的重力异常滤波匹配算法;设计了一组并行卡尔曼滤波器,并简化了最优滤波器的选择准则。不同重力特征区域的实验表明,该算法能够有效降低惯性导航系统经纬向漂移误差,提高系统的导航定位精度。 Extended Kalman filter technology can correct inertial navigation system drift error by using sequence observed gravity anomaly data and navigation area gravity anomaly reference map. In order to solve the problem of the uncertainty of observed gravity noise due to the changes of gravity measurement environment and the disturbance of measuring instruments, a gravity anomaly filter based on residual residual adaptive estimation of residual noise (RAE) Matching algorithm; a series of parallel Kalman filters are designed and the optimal filter selection criteria are simplified. Experiments on different gravity characteristic regions show that this algorithm can effectively reduce the latitude and longitude drift error of inertial navigation system and improve the navigation accuracy of the system.