Heterogeneous AAV Resource Scheduling for Dynamic Time Sensitive Target Detection and Interference

In complex electromagnetic environments, targets that need to be interfered with often possess high levels of concealment and anti-interference capabilities. Additionally, due to the dynamic characteristics of these targets, interference tasks must be conducted within strict time constraints to ensure interference effect. In this article, we adopt a reconnaissance-first approach for concealed targets. After detecting the accurate location of the target, we deploy autonomous aerial vehicles (AAVs) to interfere with the targets. First, we established a AAV swarm task scheduling optimization model after considering constraints, such as target threat range, priority of reconnaissance and interference tasks, interference task time, and AAV energy consumption. Meanwhile, we model the anti-interference capability of the target as a threat range. Second, we propose a nondominated sorting genetic algorithm based on distance in the solution space and a dynamic parent selection strategy (DPSNSGA-II) to solve AAV resource scheduling optimization problem. The diversity of the population is increased and the situation of falling into local optima is reduced by improving the parent individual selection strategy, mutation strategy, and elite solution retention mechanism. Finally, we construct two data sets of varying sizes to evaluate the quality of solution sets and the convergence performance of the proposed algorithm. The simulation results indicate that the proposed DPSNSGA-II algorithm has better result for population diversity and convergence compared to state-of-the-art algorithms.