城市密集环境下基于不确定度的无人机频谱地图主动感知方法OA

In dense urban environments,blockage by high-rise buildings,concurrent radiation from multiple wireless systems,and the superposition of multi-source interference make the electromagnetic environment highly time-varying and spatially heterogeneous.Traditional radio maps,which are constructed offline and updated at a low frequency,cannot re-flect the real channel state in time.As a result,they are unable to meet the requirements of dynamic spectrum management,wireless resource scheduling,and low-altitude communication support in terms of both accuracy and timeliness.This chal-lenge is more pronounced in complex mission scenarios,where online sensing and rapid decision-making are especially im-portant.To address this issue,this paper proposes an uncertainty-driven active sensing method for UAV(Unmanned Aerial Vehicles)-based spectrum mapping.First,the online radio map updating and path planning processes are jointly modeled as a Markov decision process.The state explicitly includes statistical measures of map uncertainty,UAV position,flight time,target distance,and mission progress,so as to improve the policy's ability to represent environmental changes and mission stages.Second,a pre-trained U-Net is employed to reconstruct the SINR(Signal to Interference plus Noise Ratio)radio map online and to output pixel-level uncertainty estimates.The reduction in uncertainty is used to characterize information gain,which guides the UAV to sense high-value unknown regions with priority.Furthermore,a FiLM-D3QN decision network based on feature-wise linear modulation is designed.It uses uncertainty and mission-progress conditions to dynamically modulate intermediate features and value estimation,thereby achieving an adaptive balance between information acquisition and trajectory efficiency.Simulation results show that,in urban scenarios with dense interference,the proposed method ef-fectively reduces map reconstruction error while maintaining an arrival rate above 90%.The RMSE(Root Mean Square Er-ror)is reduced by about 7.1% compared with the classical IPP method and by about 9.8% compared with the original D3QN.The proposed method also achieves a shorter average episode length.These results verify its effectiveness for online radio map updating and active sensing in complex urban electromagnetic environments.