Aiming to improve the position and velocity precision of the INS/GNSS system during GNSS outages, a novel system that combines unscented Kalman filter (UKF) and nonlinear autoregressive neural networks with external inputs (NARX) is proposed. The NARX-based module is utilized to predict the measurement updates of UKF during GNSS outages. A new offline approach for selecting the optimal inputs of NARX networks is suggested and tested. This approach is based on mutual information (MI) theory for identifying the inputs that influence each of the outputs (the measurement updates of UKF) and lag-space estimation (LSE) for investigating the dependency of these outputs on the past values of the inputs and the outputs. The performance of the proposed system is verified experimentally using a real dataset. The comparison results indicate that the NARX-aided UKF outperforms other methods that use different input configurations for neural networks.

中文翻译：

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一种帮助无迹卡尔曼滤波器桥接综合导航系统中 GNSS 中断的新方法

为了在GNSS中断期间提高INS/GNSS系统的位置和速度精度，提出了一种结合无迹卡尔曼滤波器（UKF）和具有外部输入的非线性自回归神经网络（NARX）的新型系统。基于 NARX 的模块用于在 GNSS 中断期间预测 UKF 的测量更新。建议并测试了一种用于选择 NARX 网络最佳输入的新离线方法。这种方法基于互信息 (MI) 理论，用于识别影响每个输出的输入（UKF 的测量更新）和滞后空间估计 (LSE)，用于调查这些输出对输入过去值的依赖性和输出。使用真实数据集通过实验验证了所提出系统的性能。