A method of unmanned aerial vehicle detection and avoidance decision based on DAIDALUS

With the rapid development of aviation technology, especially in intelligent situation awareness, autonomous flight control and electric propulsion, the number of unmanned aerial vehicles (UAVs) has grown explosively, bringing challenges to low-altitude flight safety. Autonomous UAV detect and avoid (DAA) technology is a key future direction. Firstly, this paper focuses on UAV multi-sensor data fusion, proposing an asynchronous trajectory fusion method via comprehensive weighting that accounts for multiple influencing factors. Secondly, for trajectory tracking of ownship and intruders, improved Kalman filter (KF) and enhanced Sage-Husa adaptive unscented Kalman filter (AUKF) algorithms are used for vertical and horizontal tracking to enhance accuracy. Then, a hybrid method combining detect and avoid alerting logic for unmanned aircraft systems (DAIDALUS) with Markov decision process (MDP) is proposed to solve the UAV collision avoidance decision-making problem. It calculates collision avoidance strategies via the state space, takes them as MDP’s action space, sets up reward functions and state transition probabilities to build an MDP model, and explores the influence of discount factors. Finally, through experimental tests, results show the proposed UAV detection and avoidance decision method has a 27.2% efficiency increase compared to standalone DAIDALUS, the loss of well clear (LoWC) rate is only 5.8%, and the nearest distance between the intruder and the ownship is 343 m, meeting the collision avoidance requirements for UAV flight.