A Navigation Spoofing Source Deployment Algorithm for Critical Infrastructure Under Illegal UAV Threats Utilizing Site-Adaptive NS-MOPSO with Local-Best-Driven and Directed Mutation

With the intensification of UAV (Unmanned Aerial Vehicle) “black flight” threat, the dynamic cooperative deployment of navigation spoofing jamming sites faces the triple challenges of integrity coverage, spoofing signal mutual interference suppression, and resource cost optimization. In this paper, we propose an intelligent deployment framework that breaks through the limitations of traditional algorithms of fixing the number of stations, fixing the radius, and optimizing only the location of stations. By constructing a multi-objective optimization model, we innovatively design a hybrid optimization mechanism that includes velocity-position update with local-best-driven, site-adaptive maintenance, and directed mutation. Among them, the local-best-driven module generates particle motion gradients through site-level dual-objective evaluation to solve the global guidance failure problem in dynamic scenarios; the site-adaptive maintenance module achieves site-level dynamic optimization through the judgement of site absence and redundancy; and the directed mutation module further optimizes the redundant resources through the transformation of inefficient sites. Simulation results demonstrate that this method significantly improves the reliability and economy of the deployment of the active protection system in the complex electromagnetic environment and provides theoretical support for the construction of adaptive protection networks in critical areas such as cities and coastal areas.