The integrated navigation system consists of Inertial Navigation System (INS) and receiver of Global Navigation Satellite System (GNSS). The combined system provides continuous and accurate navigation solution when compared to standalone INS or GNSS. However, the accuracy of navigation solution of the integrated system degrades during GNSS outages. Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel method (namely UKF + NARX) that combines Unscented Kalman Filter (UKF) and nonlinear autoregressive neural network with external inputs (NARX) is proposed. The NARX networks are used to predict position and velocity errors during GNSS outages. The selection of inputs of NARX networks is performed using mutual information (MI) criterion and lag-space estimation (LSE). The performance of the proposed method is experimentally verified using real dataset acquired in a land-vehicle navigation test. Results show that the proposed method outperformed UKF and other methods that use different inputs for neural networks.

集成导航系统由惯性导航系统（INS）和全球导航卫星系统（GNSS）的接收器组成。与独立INS或GNSS相比，该组合系统可提供连续且准确的导航解决方案。但是，在GNSS中断期间，集成系统的导航解决方案的准确性会降低。为了提高GNSS中断期间INS / GNSS系统的位置和速度精度，提出了一种结合无味卡尔曼滤波器（UKF）和非线性自回归神经网络与外部输入（NARX）的新方法（即UKF + NARX）。NARX网络用于预测GNSS中断期间的位置和速度误差。使用互信息（MI）标准和滞后空间估计（LSE）执行NARX网络输入的选择。使用在陆地车辆导航测试中获取的真实数据集，通过实验验证了该方法的性能。结果表明，所提出的方法优于UKF和其他为神经网络使用不同输入的方法。