With the advancement of cognitive radar, applying deep neural networks to radar jamming recognition has become an indispensable research direction. However, as a common anti-jamming measure, the agility of radar waveform parameters degrades the effectiveness of jamming recognition, creating a trade-off between jamming recognition and anti-jamming agility. Specifically, for the same type of jamming, radar agility in frequency, pulse width, and bandwidth alters the profile and scale features of the jamming, posing challenges to conventional CNN-based jamming recognition. To address this challenge, this paper proposes an Adaptive Feature-Focusing CNN (AFF-CNN). A pre-trained AFF module is designed to establish a mapping 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 calibrates deviations induced by radar inter-pulse parameter agility and enhances 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, thereby enhancing the network’s adaptability to inter-pulse parameter agility in radar systems. Simulation results demonstrate superior recognition accuracy and generalization capability compared to five comparative approaches, confirming effective adaptation to inter-pulse agility scenarios.

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.

Radar active jamming recognition is an essential component of radar anti-jamming processing and cognitive radar decision making. As jamming categories proliferate and electromagnetic environments become more complex, collecting sufficient labeled samples for every possible jamming condition can be challenging in practical scenarios. Thus, it becomes necessary to exploit the recognition capability under few-shot conditions, especially in the face of varying jamming-to-noise ratio (JNR) and compound interference. Existing methods commonly rely on a single signal representation or treat jamming categories as discrete labels, which may underuse the complementary evidence available across signal domains and the semantic knowledge associated with jamming categories. This paper proposes FuseCLIP, a semantic-guided multidomain fusion framework for few-shot radar active jamming recognition. FuseCLIP jointly encodes time-domain sequences, frequency-domain sequences, and short-time Fourier transform (STFT) spectrograms to capture waveform, spectral, and time-varying spectral characteristics. The resulting features are adaptively fused and mapped into a unified representation space, where they are matched with fixed Contrastive Language–Image Pre-training (CLIP) text prototypes generated from task-related jamming descriptions. By combining multidomain physical information with category-level semantic priors, the proposed framework is intended to make more effective use of limited labeled data. Experiments on a simulated radar active jamming dataset under different shot numbers and JNR levels demonstrate the effectiveness of FuseCLIP relative to representative baselines. Ablation experiments further indicate the complementary contributions of the time-domain, frequency-domain, and time-frequency inputs.

Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization.