Due to the varying distances of different unmanned aerial vehicles （UAVs）， the overlapping signals often exhibit different signal-to-noise ratios， and the presence of various interference signals in low-altitude environments further increases the difficulty of identification. To address these problems， this paper proposes a joint detection-separation-identification scheme for overlapping signals from multiple UAVs. The scheme effectively improves the detection and identification performance of overlapping signals with different SNRs through three steps： signal detection， signal separation， and signal identification. First， the YOLO detector is employed to locate potential UAV signals on the time-frequency spectrogram. Then， a data augmentation method based on random deviation is proposed to mitigate the bias in the signal separation process. Subsequently， the bandwidth and duration features of the signals are extracted using a YOLO-based classifier to achieve classification of distinct UAV signals. Finally， to further improve the recognition accuracy of signals from identical UAV models， an enhanced ResNet model integrated with attention mechanisms and an optimized Bagging ensemble learning method are proposed. Experimental results based on publicly available datasets demonstrate that the proposed scheme outperforms existing methods in scenarios where interference signals and UAVs of the same model coexist.