神经网络修正的速度约束辅助车载SINS定位算法

对于车载全球导航卫星系统(GNSS)/捷联惯性导航系统(SINS)组合导航系统，针对GNSS失效而SINS单独工作时仅使用速度约束辅助SINS其纵向位置误差逐渐发散的问题，提出一种神经网络修正的速度约束辅助车载SINS定位算法。通过径向基函数(RBF)神经网络预测SINS纵向位置误差修正系数，以提高SINS单独工作时的定位精度；此外，提出一种限定记忆指数加权实时估计量测噪声的自适应滤波算法。在人为设置GNSS失效以及真实隧道场景下进行车载试验，结果表明本文算法能够在不停车情况下在线修正SINS纵向位置误差，相比于速度约束与卡尔曼滤波相结合的常规算法，有效地提高了GNSS失效时的车载SINS定位精度。

Vehicle SINS Positioning Algorithm Assisted by Velocity Constraint Based on Neural Network Modification

For the vehicle mounted global navigation satellite system (GNSS)/strapdown inertial navigation system (SINS) integrated navigation system, aiming at the problem of gradual divergence of longitudinal position error of SINS assisted by velocity constraint when GNSS fails and SINS works alone, a vehicle SINS positioning algorithm assisted by velocity constraint based on neural network madification is proposed. The radial basis function (RBF) neural network is used to predict the correction coefficient of SINS longitudinal position error, so as to improve the positioning accuracy of SINS when working alone. In addition, an adaptive filtering algorithm for real time measurement noise estimation with limited memory index weighting is proposed. The vehicle tests are carried out under artificially setting GNSS failures and real tunnel scenarios. The results show that the proposed algorithm can correct the longitudinal position error of SINS online without stopping. Compared with the conventional algorithm combining velocity constraint and Kalman filter, the positioning accuracy of vehicle SINS under GNSS failure is effectively improved.