There are many problems in present anti-stealth detection technologies, such as small detection range and excessive detection blind zone. Besides, the stealth target is difficult to be detected and tracked with current technology. To solve these problems, this paper presents a novel anti-stealth method that is based on the thought of swarm and makes use of the more flexible airborne platform to generate and change formation configuration. First, the cooperative anti-stealth airspace coverage model of aircraft swarm is established, and an improved airspace coverage solution method is proposed. Second, four evaluation indexes and effective detection airspace are defined, meanwhile, the optimization objective function is obtained by using the weighted sum. Third, based on the study of the one transmitter and one receiver configuration, we optimize the 1TnR spatial configuration by using the rule-searching strategy and particle swarm optimization algorithm. Finally, the effectiveness of the proposed model and the feasibility of the algorithm are verified by simulation examples.

This paper addresses the formation assignment problem for heterogeneous UAV swarms in dynamic mission environments. A constrained optimization model is constructed in which UAV capabilities are divided into shareable and exclusive types; a neighborhood collaboration decay factor captures the locality of capability complementarity; and a Cobb–Douglas production function evaluates position-specific effectiveness under bottleneck constraints. The objective dynamically trades off deployment costs and system risks through threat-adaptive weight adjustment. To solve the model, a Hybrid Adaptive Large Neighborhood Search (HALNS) algorithm is proposed, integrating an adaptive destroy-repair mechanism, a mathematical-programming-based local search, and an incremental re-optimization strategy for rapid dynamic response. Experiments verify that HALNS attains globally optimal solutions on small-scale instances and outperforms mainstream baselines on medium-to-large problems. The collaboration mechanism raises system effectiveness by an average of 34.75% across four mission scenarios. Compared with static re-optimization, the incremental strategy improves dynamic response performance by 58.25% while reducing runtime by up to 56.7%. Sensitivity analyses confirm the robustness of key parameters. This work provides a theoretical and algorithmic foundation for intelligent UAV swarm assignment and reconfiguration.