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Aiming at the resource optimization problem in the cooperative detection task, the objective function is constructed based on the channel capacity, and the artificial bee colony (ABC) algorithm is improved to realize the joint optimization of the UAV swarm trajectory and radiation power. Firstly, a multiple input multiple output (MIMO) cooperative detection model is constructed. Then, based on the perspective of information theory, the channel capacity of the cooperative detection model is derived and used as the objective function for optimizing the detection resources of UAV swarm. Then, the factors affecting the objective function are sorted out and analyzed one by one, and the constraints are clarified. Aiming at the shortcomings of ABC algorithm, its search strategy and parameter optimization method are improved. A joint optimization process of UAV swarm trajectory and radiated power based on improved ABC algorithm is constructed. Finally, through simulation verification and algorithm comparison, it shows that the algorithm in this paper can improve the perception ability of cooperative detection of UAV swarm.

Aiming at the problem that the air traffic flow is increasing year by year and the flight conflicts are difficult to be deployed, we take aircraft as the node and established a flight conflict network based on the flight conflict relationship between aircrafts. After that, we define the concept of an optimal dominating set. By removing the optimal dominating set nodes of the flight conflict network, the conflicts in the network can be quickly resolved and the complexity of the network is reduced. In the process of solving the optimal dominating set of the network, we introduce the immune mechanism based on the particle swarm algorithm (PSO) and ensure the priority deployment of a critical aircraft and high-risk conflicts by setting two types of antigens, nodes and connected edges. Compared with the traditional method, the conflict resolution strategy presented in this paper is able to quickly identify key aircraft nodes in the network and has better sensitivity to high-risk conflict edges, which can provide controllers and the control system with a more accurate and reliable suggestion to resolve the flight conflicts macroscopically.