An End-to-End Radio Signal Drone Detection and Recognition Algorithm Based on Hard Example Mining

Radio signal drone detection and recognition, which is widely applied in radio monitoring and radio countermeasures, is an important direction in the intelligent processing of radio signals. This paper proposes an end-to-end algorithm for radio signal drone detection and recognition based on hard example mining. First, a novel backbone network for one-dimensional radio frequency spectrum data is devised by optimizing VGG network. Subsequently, multi-scale features are employed to address the scale variation in the frequency domain of radio signal targets. Finally, a new cross-entropy loss function in conjunction with a negative sample enhancement strategy is proposed to improve the performance on challenging examples. Experimental results demonstrate that,in comparison to existing methodologies, the proposed approach exhibits superior accuracy and efficiency in detecting and recognizing radio signal targets.