Autonomous Flight Strategy Selection and Interval Maintenance for Aircraft With Unknown Flight Intentions

To enhance the operational safety and efficiency of aircraft under uncertain or unknown flight intentions, a decision-making framework based on Markov Decision Processes with incomplete information (IIG-MDP) is proposed in this paper. Firstly, the paper incorporates the potential impact of the target aircraft and its surrounding traffic flow into the decision-making assessment, calculating the components of the decision model's state space, among other elements. Secondly, the continuous episode is discretized into a multi- episode space, transforming the interval maintenance problem into a discrete multi-episode decision-making problem, and at the beginning of each episode, the action estimate for the target is corrected based on the observed state of the target. Thirdly, an episode is further discretized into a series of decision moments, and an n-Step Approximate Dynamic Programming (ADP) algorithm is proposed to calculate the payoff value of action strategies at each decision moment, obtaining the optimal decision sequence within an episode, and then updating the initial state of the next episode for iterative calculation until the end of the flight. Through simulation experiments, the IIG-MDP model and algorithm are verified, and the results show that the 3-Step ADP algorithm used in this paper can significantly reduce the computational dimension of the dynamic programming method, improving computational efficiency. Compared with the Monte Carlo and MPC decision-making models, it offers better decision choices while ensuring safety. Due to the prediction and updating of the target's state, this method also has better real-time performance and practicality.