Jamming Recognition Based on Adaptive Feature Focusing Convolutional Neural Network For Agile Cognitive Radar

With the advancement of cognitive radar, the application of deep neural networks for radar jamming recognition has become an indispensable research. However, as a common anti-jamming measure, the agility of radar waveform parameters will degrade the effectiveness of jamming recognition, resulting in a effectiveness dilemma to balance jamming recognition, and anti-jamming agility. Specifically, for the same type of jamming, the agility of the radar in frequency, pulse width and bandwidth will alter the profile and scale features of the jamming, thus posing challenges to jamming recognition based on conventional CNN networks. To address this challenge, this paper proposes an Adaptive Feature-Focusing CNN (AFF-CNN), a pre-trained AFF module is designed to establish the mapping relationship between agile parameters and adaptive feature scales. Operating on time-domain high-resolution range profiles (HRRP) and time-frequency domain short-time Fourier transform (STFT) data, this module contributes to calibrating the deviations induced by radar inter-pulse parameter agility while enhancing the capability of salient signal feature focusing. Furthermore, a lightweight 1D-2D feature fusion CNN is designed to process these adaptive features and recognize jamming using single-pulse signals, thus enhancing the network's adaptability to inter-pulse parameter agility in radar systems. Simulation results demonstrate superior recognition accuracy and generalization capability over four comparative approach, confirming effective adaptation to inter-pulse agility scenarios.