Deep Reinforcement Learning (DRL) methods are inefficient in the initial strategy exploration process in large-scale complex scenarios. This is becoming one of the bottlenecks in their application to large-scale game adversarial scenarios. This paper proposes a Safe reinforcement learning combined with Imitation learning for Task Assignment (SITA) method for a representative red-blue game confrontation scenario. Aiming at the problem of difficult sampling of Imitation Learning (IL), this paper combines human knowledge with adversarial rules to build a knowledge rule base; We propose the Imitation Learning with the Decoupled Network (ILDN) pre-training method to solve the problem of excessive initial invalid exploration; In order to reduce invalid exploration and improve the stability in the later stages of training, we incorporate Safe Reinforcement Learning (Safe RL) method after pre-training. Finally, we verified in the digital battlefield that the SITA method has higher training efficiency and strong generalization ability in large-scale complex scenarios.

As military technology continues to evolve and the amount of situational information available on the battlefield continues to increase, data-driven deep learning methods are becoming the primary method for air target intention recognition. Deep learning is based on a large amount of high quality data; however, in the field of intention recognition, it often faces key problems such as low data volume and unbalanced datasets due to insufficient real-world scenarios. To address these problems, we propose a new method called time-series conditional generative adversarial network with improved Hausdorff distance (IH-TCGAN). The innovation of the method is mainly reflected in three aspects: (1) Use of a transverter to map real and synthetic data into the same manifold so that they have the same intrinsic dimension; (2) Addition of a restorer and a classifier in the network structure to ensure that the model can generate high-quality multiclass temporal data; (3) An improved Hausdorff distance is proposed that can measure the time order differences between multivariate time-series data and make the generated results more reasonable. We conduct experiments using two time-series datasets, evaluate the results using various performance metrics, and visualize the results using visualization techniques. The experimental results show that IH-TCGAN is able to generate synthetic data similar to the real data and has significant advantages in the generation of time series data.

Spatial crowdsourcing is a mode that uses distributed artificial computing power to solve specific function sets through Internet outsourcing. It has broad application value in the networked command and control of current joint air defense operations. In this paper, we introduce the spatial crowdsourcing theory into the field of target allocation for joint air defense operations and establish a weapon-target assignment model based on spatial crowdsourcing mode, which is more appropriate to the real situation and highlights the system cooperation capability of joint air defense operations. To solve the model, we propose a heuristic variable weight nonlinear learning factor particle swarm optimization (VWNF-PSO). This algorithm can significantly improve the efficiency and adaptability to weapon-target assignment problems under large-scale extreme conditions. Finally, we establish two kinds of joint air defense operation scenarios to verify the proposed model, then compare the proposed algorithm with variable weight PSO (VWPSO) and adaptive learning factor PSO (AFPSO), to validate the effectiveness and efficiency of the VWNF-PSO algorithm proposed in this paper.

This paper investigates the Air Defense Kill Chain Scheduling (ADKCS) problem, a typical large-scale, highly constrained, and multi-objective combinatorial optimization problem. First, a multi-objective optimization model is established to minimize the average kill chain closure time and resource load imbalance. Characterized by complex spatiotemporal constraints and tight resource coupling, ADKCS presents significant challenges to existing evolutionary algorithms, which often struggle with searching feasible solutions and suffer from slow convergence. To address these issues, we propose a Hybrid Graph Autoencoder-based Adaptive Lamarckian Memetic Algorithm (HGAE-ALMA). The proposed HGAE-ALMA integrates a graph representation learning mechanism with an adaptive infeasible solution repair strategy. Specifically, to mine the latent topological relationships between resources and targets, a Graph Autoencoder (GAE) is designed to learn low-dimensional embeddings of the problem structure via Graph Convolutional Networks (GCN) in an unsupervised manner, generating an Affinity Matrix to guide population mutation. Furthermore, an Adaptive Lamarckian Repair Strategy is introduced, combining heuristic correction of infeasible solutions with adaptive parameter control based on the population feasibility ratio. This strategy prioritizes feasible region exploration in the early evolutionary stages while preserving population diversity in the later stages. Large-scale computational experiments demonstrate that in high-intensity confrontation scenarios, the two proposed improvement strategies increase the Hypervolume (HV) indicator by 21.8% and 23.5%, respectively. Furthermore, HGAE-ALMA exhibits a significant advantage in feasible solution search efficiency (EFF), reducing the evaluations required to find the first valid schedule by up to 98.4% (representing a 24- to 62-fold reduction) compared to traditional evolutionary algorithms.

This paper briefly analyzed the operational background and characteristics of regional joint air defense, summarized the requirements of mission planning for regional joint air defense operations, and pointed out the deficiencies of current research. Finally, it is suggested that the command level should be adjusted, the charging mode should be optimized, the target allocation model and solving algorithm should be improved, and the evaluation system of joint air defense operation should be improved in the future development of joint air defense operation.