Active Jamming Mitigation for Short-Range Detection System

The emergence of active jamming technology has led to a surge of interest in jamming suppression. This work proposes a one-dimensional adaptive multi-scale residual autoencoder (1-DAMRAE) to take overlapping signals (echo signals and jamming signals) as input and restore correspondingly-sized echo signals, which frames the jamming suppression as a regression problem. 1-DAMRAE incorporates three key components. First, adaptive multi-scale convolutional blocks are developed to adaptively fuse multi-scale information for a more robust representation learning. Second, a fully residual design is explored to improve information flow within and across blocks. Third, given the unknown of jamming sources, our method encodes contextual dependencies and produces a plausible hypothesis for the potential missing information to handle out-of-distribution (OOD) jamming. To verify the reliability of 1-DAMRAE, the dynamic short-range detection system model is established. Experimental results show that 1-DAMRAE achieves excellent anti-jamming performance while keeping complexity and computation cost at a low level. Even facing unseen OOD jamming, 1-DAMRAE can still achieve desirable anti-jamming performance.