RDSAN-CDS: An RD Spectrum-Aware Network With Controllable Detection Sensitivity for HFSWR Target Detection

High-frequency surface wave radar faces severe challenges from complex interference in maritime monitoring, hindering weak target detection and false alarm control. To address the model mismatch inherent in traditional constant false alarm rate detectors and the limited perception and lack of sensitivity adjustability in existing encoder-decoder deep neural network methods, this article proposes a Range-Doppler (RD) spectrum-aware network with controllable detection sensitivity (RDSAN-CDS). The network adopts an end-to-end encoder-decoder architecture, integrating Transformer modules at the deep feature extraction stage to capture the global long-range dependences between targets and the environment within the RD spectrum. Furthermore, a gating mechanism driven by a preset false alarm probability is designed as an auxiliary input channel. This mechanism dynamically adjusts multiscale feature responses during decoding, endowing the network with flexible detection sensitivity control during inference. Experimental results on field-measured radar data demonstrate that RDSAN-CDS outperforms existing mainstream methods such as residual regression network and DeepLabv3 in complex clutter backgrounds, achieving an effective balance and precise control of the detection rate and false alarm rate under diverse mission requirements.