Research on Interstellar Autonomous Navigation Method Based on the Improved QLEKF Algorithm

This dataset supports the research on an improved Q-learning based Extended Kalman Filter (QLEKF) for interstellar autonomous navigation. It includes simulation results of three algorithms: EKF, QLEKF, and QLEKF-step, under varying noise parameters and configuration settings. The dataset consists of the following components: RMSE/ Contains three files that record the root mean square errors (RMSE) of position estimates across 50 simulation runs for each of the three algorithms. The RMSEs are evaluated under changes in:.mat epsilon: RMSE_epsilon.mat gamma: RMSE_gamma.mat Observation noise scaling factor: RMSE_r.mat SE/ Includes four files representing the standard errors (SE) and covariance matrix components of position estimates for the QLEKF-step algorithm:.mat Position_std_error.mat: Standard deviation of position estimates PX.mat, , : Covariance matrix components over time steps 1–12000PY.matPZ.mat time/ A file that reports the execution time (in seconds) of 50 independent simulation runs for EKF, QLEKF, and QLEKF-step.Runtime.xlsx