A Cross-Modal Multi-Attitude Framework for the Generation of Space Target ISAR Images

Abstract

Inverse Synthetic Aperture Radar (ISAR) imagery of space targets exhibits superior physical fidelity and satisfactory textural representation of components in ISAR images of targets, even under conditions characterized by sparse input optical samples. This paper introduces an innovative optical-to-radar cross-modal framework for the generation of full-attitude, high-fidelity space target ISAR samples, denominated as AORC. Specifically, the attitude encoding module (AEM) assimilates the prior knowledge of analogous targets across different attitudes through a fine-designed NeRF-based encoder, subsequently deriving the encoded features in the latent space. Subsequently, these comprehensive attitude features are input into the modality transformation module (MTM) to undergo a Brownian-Bridge-based diffusion process, facilitating the transformation between optical and ISAR modalities for each feature from each attitude. Extensive simulations on satellite targets validate the effectiveness of the proposed approach.