Modeling and Validation of Electrostatic Sensing for UAV Targets in High-Dynamic Encounter Scenarios.

Unmanned aerial vehicles (UAVs) are increasingly used in urban management and public services, but their potential misuse poses serious risks to public safety. Electrostatic sensors offer a promising approach for UAV detection and interception by capturing their electrostatic signatures during dynamic encounters. However, the sensor output is affected by the coupling between encounter parameters and circuit characteristics, making accurate modeling challenging. This study proposes an analytical modeling method for electrically floating electrostatic sensor signals, calibrated under actual boundary conditions. The model incorporates the effects of encounter angle, miss distance, relative velocity, and equivalent input resistance-capacitance parameters, enabling efficient prediction of sensor signals under multivariable coupling. To validate the model, the electrostatic signatures during dynamic encounters were obtained using the airborne data acquisition and storage system. Results show that the predicted signals correlate well with measured data, with a correlation coefficient above 0.9. The proposed model demonstrates high computational efficiency and supports the design and optimization of electrostatic sensing systems for low-altitude UAV detection and interception.