为使基于微机电系统的捷联惯性导航/全球定位（MEMSSINS/GPS）组合导航系统在GPS接收机无法正常工作时，仍能提供满足精度要求的导航信息，提出了径向基函数神经网络(RBFNN)辅助自适应卡尔曼滤波(AKF)的信息融合方法。首先，基于该方法设计了由神经网络训练与预测两种模式构成的组合导航系统。在GPS可用时，对RBFNN进行在线训练；在GPS失锁时，由RBFNN预测AKF更新过程的量测输入。然后，建立了RBFNN与AKF的数学模型，并设计了RBFNN的训练策略与AKF的自适应算法。最后，通过跑车实验验证了该信息融合方法的有效性。实验结果表明，在GPS断开时间为40 s和100 s时，系统的位置精度分别优于15 m和90 m。该信息融合方法能在GPS失锁时对导航误差发散进行有效阻尼，是适用于小型无人机、制导炸弹与车辆的一种低成本、高鲁棒性、中等精度的导航方案。

To allow Microelectromechanical System(MEMS)based SINS/GPS integrated navigation systems to meet the accuracy requirements during GPS outages, a Radial Basis Function Neural Network (RBFNN) aided Adaptive Kalman Filtering (AKF) information fusion method was proposed.Firstly,the system structure consisting of dual modes of RBFNN training and prediction was designed.The RBFNN was trained while GPS signals were available and the inputs for AKF measurement updates were predicted during the GPS outages.Then，the mathematic models for RBFNN and AKF were built and the training strategy for RBFNN and the adaptive algorithm for AKF were designed.Finally, the performance of the proposed information fusion method was validated using real field test data.Test and experiment results show that the position precisions are better than 15 m and 90 m during GPS outages at 40 s and 100 s,respectively.The proposed information fusion method can effectively damp the divergence of the navigation error during GPS outages and can provide a lowcost, highrobustness, and mediumaccuracy navigation scheme for small Unmanned Aerial Vehicles(UAVs),guided bombs and land vehicles.