Electromagnetic target detection model for unmanned aerial vehicle platforms based on Bayesian detection

Existing models for target detection on unmanned aerial vehicle platforms largely rely on the Boolean perception model. However, aiming at electromagnetic radiation sources, the Boolean perception model fails to accurately reflect the propagation and detection characteristics of electromagnetic wave signals. To address this issue, this paper, grounded in the Bayesian signal detection theory and utilizing the distributional characteristics of antenna pattern diagrams, constructs an electromagnetic detection model for unmanned aerial vehicle platforms. The performance of the novel model is derived and analyzed under a scanning coverage search algorithm. Analytical and simulation results indicate that the proposed model effectively describes the process of electromagnetic target detection. In coverage search tasks, when the unmanned aerial vehicle is equipped with the KSHA-BJ32-10-NH antenna, the new model has a shorter range compared to the Boolean perception model, and the advantage of the new model becomes more pronounced as the search area expands. The increase in the required range for the unmanned aerial vehicle to complete the task is only 85.41% of that of the traditional Boolean model.