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Space-time adaptive processing (STAP) is an important radar and sonar technique that can be used to suppress clutter and jamming. However, traditional constant false alarm rate (CFAR) cascade detection methods are difficult to provide explicit location and number of targets and jammings, while general purpose data-driven object detectors usually consume a large number of floating point operations (FLOPs) and parameters. To deal with this problem, we propose a new idea to design a dedicated data-driven object detector to predict bounding boxes and class probabilities directly from power images of STAP (STAPDet) in this letter. This idea embraces the characteristics of STAP to customize the detector architecture. Specifically, STAPDet first proposes an ultra-lightweight backbone part to effectively recognize the obviously different STAP objects. Second, the proposed detector enlarges the receptive field of detection head to cover the limited scales of the STAP objects instead of using the complicated neck part to fuse multi-scale features. Last, STAPDet adopts the single detection head to predict the sparse STAP objects with better simplicity and fewer parameters. Experiments on real-world data demonstrate that STAPDet provides accurate location and number information of objects while greatly reducing computational complexities and parameters compared with existing state-of-the-art counterparts. These results validate the effectiveness of our idea and suggest a new perspective to design efficiently dedicated detectors.
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