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In the complex electromagnetic environment of the 230-270MHz ultra short wave frequency band, traditional energy detection methods suffer from missed detections and high false alarm rates in broadband satellite signals. This paper proposes a broadband ultra short wave signal detection method based on the Short Cut Swin Transformer YOLOV5s (SST-YOLOV5s) network with spectrum superposition, Effectively addressing the challenge of detecting broadband satellite channels in low signal-to-noise ratio scenarios, a problem often encountered with traditional methods. Additionally, tackling the issue of elevated false alarm rates when interference anomalies are present. Firstly, by overlaying spectra, the discrimination between ultra short wave signals and bottom noise is highlighted, and the influence of short burst interference is suppressed, Enhancing the target signal characteristics effectively amidst a low signal-to-noise ratio. Simultaneously, a four layer SC (shortcut)-ST (Swin Transformer) and multi-layer convolutional cascaded ultra short wave signal feature extraction backbone network SST-Backbone (SC-ST-Backbone) are proposed. In the backbone network, the SC-ST module utilizes the global attention to global features of the Transformer, combined with residual multi-layer convolution modules that focus on local features, to increase the depth and receptive field of the network, making the network model more accurate in reconnaissance and detection of broadband ultra short wave signals in the target frequency band. It can efficiently remove the interference of bottom noise features and reduce the attention to abnormal signal features, Improved the detection accuracy of broadband ultra short wave target signals in complex environments and reduced false alarm rates.
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