Nowadays, the Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integrated navigation system is widely used in many applications. The extended Kalman Filter (EKF) is a popular data fusion method for the INS/GNSS integrated navigation system. However, the process and measurement noise covariance matrices of the EKF cannot be modelled accurately due to varied scenes and complicated GNSS signal errors in urban environments, which undermines or deteriorates the EKF's performance. To mitigate noise covariance uncertainties' influence, this paper proposes an adaptive EKF algorithm named SARSA EKF, which enables the State-Action-Reward-State-Action (SARSA) method in EKF to realise the autonomous selection of the noise covariance matrices based on the Q-value. Meanwhile, a pruning algorithm is designed to remove inappropriate selections of noise covariance matrices and enhance the performance. The simulation and field test results indicate that the positioning accuracy of the SARSA EKF is better than the traditional EKF and the Q-learning EKF (QLEKF). The positioning accuracy's mean error of the SARSA EKF decreases by 34.32% and 25.95% compared with the traditional EKF and the QLEKF, respectively. And the positioning accuracy's standard deviation of the SARSA EKF decreases by 41.74% and 32.99% compared with the traditional EKF and the QLEKF, respectively.

如今，惯性导航系统/全球导航卫星系统（INS/GNSS）组合导航系统被广泛应用于许多应用领域。扩展卡尔曼滤波器 (EKF) 是一种流行的 INS/GNSS 组合导航系统数据融合方法。然而，由于城市环境中场景的变化和复杂的 GNSS 信号误差，EKF 的过程和测量噪声协方差矩阵无法准确建模，这会破坏或恶化 EKF 的性能。为了减轻噪声协方差不确定性的影响，本文提出了一种名为 SARSA EKF 的自适应 EKF 算法，该算法使 EKF 中的状态-动作-奖励-状态-动作（SARSA）方法能够实现基于噪声协方差矩阵的自主选择。 Q值。同时，a pruning algorithm is designed to remove inappropriate selections of noise covariance matrices and enhance the performance. 仿真和现场测试结果表明，SARSA EKF的定位精度优于传统的EKF和Q-learning EKF（QLEKF）。与传统EKF和QLEKF相比，SARSA EKF的定位精度平均误差分别降低了34.32%和25.95%。SARSA EKF的定位精度标准差比传统EKF和QLEKF分别降低了41.74%和32.99%。与传统EKF和QLEKF相比，SARSA EKF的定位精度平均误差分别降低了34.32%和25.95%。SARSA EKF的定位精度标准偏差与传统EKF和QLEKF相比分别降低了41.74%和32.99%。与传统EKF和QLEKF相比，SARSA EKF的定位精度平均误差分别降低了34.32%和25.95%。SARSA EKF的定位精度标准偏差与传统EKF和QLEKF相比分别降低了41.74%和32.99%。