Impact of UAV Attitude Perturbations on Friendly Jamming Performance in Dynamic Networks

This investigation examines how drone vibrations affect anti-eavesdropping capabilities in mobile UAV networks using multipath radio models. We construct a unified framework covering single and multi-drone operations, evaluating both authorized communications and security jamming links. Drone motions incorporate slow random wobbles (Wiener process) and fast periodic shakes (sinusoidal patterns). Key insights emerge from channel correlation analysis: 1) Minor drone vibrations in single-unit operations drastically shorten signal stability periods, creating phase errors that weaken jamming precision. 2) Coordinated drone groups induce spatial-temporal signal mismatches, worsening eavesdropper reception instability despite maintaining individual channel independence. 3) Excessive jamming power disrupts normal communication reliability by altering signal power ratios. These results expose critical tradeoffs between jamming intensity and communication integrity. Our findings advocate for two corrective measures: real-time phase adjustment protocols and intelligent spectrum allocation strategies that account for drone movement patterns. This work provides fundamental guidelines for enhancing wireless security in vibration-prone UAV environments.