Chaff jamming recognition and suppression based on semi-realistic dataset and Pre-Decluttering Dual-Stage UNet

Countermeasures for chaff jamming have drawn great attention in the field of radar target detection and tracking. Current approaches for chaff jamming recognition and suppression exhibit limitations in practical effect, generalisation ability, and hybrid jamming handling. To address the above problems, the authors first transform the traditional 1D signal processing problem into a 2D semantic segmentation task and then solve it from the perspective of the dataset construction and algorithm design. For the dataset construction, the authors use both measured and simulated data to synthesise a more realistic labelled dataset (semi-realistic dataset), which is also with good diversity due to its adjustable chaff interference background. For the algorithm design, the authors propose a Pre-Decluttering Dual-Stage UNet (D²UNet) to recognise and suppress chaff jamming in two stages successively, where the former provides prior attention masks for the latter. To further improve the performance of D²UNet, the authors also design a multi-stage loss function to achieve progressive training. Extensive experimental results demonstrate that D²UNet delivers remarkable recognition accuracy (99.305%) and suppression performance (41.326 dB peak signal-to-jamming ratio, 0.9952 structure similarity index measure) on the semi-realistic dataset. Its practical effect is further verified on measured data.