Abstract: Jupiter exploration is of critical importance for comprehending the solar system’s evolution and utilizing planetary gravity for deep space missions. To address the challenge of noise uncertainty affecting filtering performance in Jupiter’s complex environment, an optical autonomous navigation scheme is established based on the relative line-of-sight information from multiple Jovian moons, using a simplified QLEKF（Q-Learning Extended Kalman Filter）algorithm with a single filter to estimate the position and velocity of the probe. The QLEKF-single（Single Filter Q-learning Extended Kalman Filter）designs a reward function based on the innovation of a single EKF filter. The Q-learning algorithm adaptively selects the values of the noise covariance matrix, while the SoftMax strategy is employed for action selection, ultimately achieving iterative system state estimation by EKF filter. Through simulation by randomly generating initial state estimates and measurement noise, the simplified model of Jupiter's real orbital dynamics was verified. It demonstrated that in scenarios with noise uncertainty, the QLEKF-single algorithm effectively improved navigation accuracy compared to traditional filtering methods. Moreover, compared to the QLEKF algorithm, the run time was reduced by more than 10% with little change in accuracy.