Nowadays, unmanned aerial vehicles (UAVs) pose a significant challenge to air defense systems. Unmanned combat aerial vehicles (UCAVs) have been proven to be an effective method to counter the threat of UAVs in application. Therefore, maneuver decision-making has become the crucial technology to achieve autonomous air combat for UCAVs. In order to solve the problem of maneuver decision-making, an autonomous model of UCAVs based on the deep reinforcement learning method was proposed in this paper. Firstly, the six-degree-of-freedom (DoF) dynamic model was built in three-dimensional space, and the continuous actions of tangential overload, normal overload, and roll angle were selected as the maneuver inputs. Secondly, to improve the convergence speed for the deep reinforcement learning method, the idea of “scenario-transfer training” was introduced into the twin delayed deep deterministic (TD3) policy gradient algorithm, the results showing that the improved algorithm could cut off about 60% of the training time. Thirdly, for the “nose-to-nose turns”, which is one of the classical maneuvers for experienced pilots, the optimal maneuver generated by the proposed method was analyzed. The results showed that the maneuver strategy obtained by the proposed method was highly consistent with that made by experienced fighter pilots. This is also the first time in a public article that compared the maneuver decisions made by the deep reinforcement learning method with experienced fighter pilots. This research can provide some meaningful references to generate autonomous decision-making strategies for UCAVs.

In response to the saturated attacks by low, slow, and small UAV swarms, there is currently a lack of effective countermeasures. Counter-UAV swarm technology is an important issue that urgently requires breakthroughs. This paper conducts research on a mid–short-range hard-kill counter-swarm scenario where fewer swarms confront multiple swarms and stronger swarms confront weaker swarms. The requirement is for counter-swarm UAVs to quickly penetrate the swarm at mid–short range and collide with as many incoming UAVs as possible to destroy them. To address the sparse solution space problem, an improved genetic algorithm that integrates multiple strategies is adopted to calculate the spatial density distribution of the incoming swarm. A baseline is identified through gradient descent that maximizes the density integral in a straight-line direction. Based on this baseline, the solution space for single strikes on the swarm is filtered. During the solution process, an elite strategy is introduced to prevent the overall degradation of the population performance. Additionally, the feasibility of the flight trajectory needs to be assessed. A piecewise cubic spline interpolation method is used to optimize the flight trajectory, minimizing the maximum curvature. Ultimately, multiple counter-swarm UAV targets within the swarm and their corresponding trajectories are obtained.

This paper introduces a swift method for intercepting the state trajectory of large-scale invading drone swarms using quadrotor drones. The research primarily concentrates on the design and computation of multi-target interception trajectories, with an analysis of the trajectory state constraints inherent to multi-target interception tasks. Utilizing Pontryagin’s principle of motion, we have designed computationally efficient motion primitives for multi-target interception scenarios. These motion primitives’ durations have informed the design of cost matrices for multi-target interception tasks. In contrast to static planar scenarios, the cost matrix in dynamic scenarios displays significant asymmetry, correlating with the speed and spatial distribution of the targets. We have proposed an algorithmic framework based on three genetic operators for solving multi-target interception trajectories, offering certain advantages in terms of solution accuracy and speed compared to other optimization algorithms. Simulation results from large-scale dynamic target interception scenarios indicate that for an interception task involving 50 targets, the average solution time for trajectories is a mere 3.7 s. Using the methods proposed in this paper, we conducted a comparative analysis of factors affecting the performance of interception trajectories in various target interception scenarios. This study represents the first instance in existing public research where precise evaluations have been made on the trajectories of drone interceptions against large-scale flying targets. This research lays the groundwork for further exploration into game-theoretic adversarial cluster interception methods.

This article introduces a rapid interception trajectory generation algorithm tailored for the mitigation of malicious drone activities and other high-speed airborne threats. The proposed method facilitates a high degree of flexibility in defining the terminal state parameters, including position, velocity, and acceleration, as well as the anticipated duration of drone maneuvers, thereby enabling the fulfillment of a variety of mission objectives. The approach employed in this study linearizes the aerodynamic resistance model and computes an efficient closed-form solution for the optimal trajectory motion primitive by applying Pontryagin’s Maximum Principle. Concurrently, it minimizes the cost function associated with the aggression of control inputs. The motion primitive is defined by the combination of the initial and terminal states of the drone, as well as the expected movement time. An efficient input feasibility verification method has been designed for the optimal trajectory. This algorithm can serve as a low-level trajectory generator for advanced task planning methods. After compilation, it can evaluate and compare thousands of motion primitives per second on a personal portable computer, thereby achieving certain advanced goals. The reliability of the algorithm is verified by setting up a multi-objective approach task in a physical simulation environment.