With the development of jamming technologies, jammers can respond to radar signals within a short period, causing radar frequency-hopping strategies to fail. In the face of jammers with instantaneous frequency measure...
With the development of jamming technologies, jammers can respond to radar signals within a short period, causing radar frequency-hopping strategies to fail. In the face of jammers with instantaneous frequency measurements (IFM) capabilities, radio frequency (rF) screening techniques are an effective countermeasure. However, in practice, the jamming strategy is usually unknown to the radar. Moreover, when the jammer's strategy changes, failing to promptly adjust the shielding pulse parameters will result in a significant degradation of the rF screening anti-jamming effectiveness. To overcome the limitations caused by the lack of information about the jammer and the non-stationary environment due to changes in the jammer's IFM time, this paper proposes an online radar screening pulse width allocation method based on a non-stationary multi-armed bandit (MAB). This method combines discounted historical rewards and sliding window rewards, allowing it to better adapt to the non-stationary jamming environment. Simulation results show that the proposed method has a faster convergence speed than the discounted method and a stronger exploration capability than the sliding window method. It can effectively enhance radar performance in countering instantaneous frequency measuring jammers in non-stationary jamming environments.
Limited by the scarcity of synthetic aperture radar (SAr) systems, image augmentation is of great significance to SAr image detection, target recognition, and other application fields. However, traditional image augme...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
Limited by the scarcity of synthetic aperture radar (SAr) systems, image augmentation is of great significance to SAr image detection, target recognition, and other application fields. However, traditional image augmentation methods rarely consider the SAr imaging mechanism, resulting in the inability to accurately reflect the anisotropic characteristics of target scattering. This paper introduces a novel SAr image augmentation method based on rebooting auxiliary classifier generative adversarial networks (re-ACGAN), named IAM-ACGAN (Integrating Attention Mechanism with ACGAN). In this scheme, IAM-ACGAN integrates two attention mechanisms, channel attention (CA) and spatial attention (SA), into the discriminator of the GAN backbone to enhance classification accuracy. These two mechanisms can enhance the channel and spatial features of the input SAr images respectively. A self-constructed simulation ship dataset and a MSTArreal dataset both demonstrate the effectiveness of IAM-ACGAN. Compared with ACGAN andre-ACGAN augmentation methods, IAM-ACGAN can provide higher image generation accuracy.
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