Neural networks continue to face challenges in effectively extracting information from the high-dimensional and sparse range-azimuth-Doppler (RAD) tensor. While multi-view architectures using three-axis projections of the RAD tensor improve efficiency and maintain accuracy, existing designs suffer from inefficient inter-view information interaction. To address this issue, this letter proposes a new multi-view information fusion viewpoint: instead of directly blending feature maps, the orthogonal relationships among the RAD coordinate axes are leveraged to guide feature decomposition across views, thereby disentangling the mixed semantics introduced during view compression. Based on this idea, a new module, termed Cross-View Guided Disentangler (CVGD), is introduced to enable inter-view information interaction in multi-view architectures. Extensive experiments on the public CARRADA dataset demonstrate that networks incorporating the proposed module achieve performance comparable to state-of-the-art methods, while utilizing 50% fewer parameters and attaining a 6-fold increase in inference speed. Additionally, promising results are also observed on RADIal, achieving near-SOTA performance with higher efficiency.

Unmanned combat aerial vehicle (UCAV) within-visual-range (WVR) engagement, referring to a fight between two or more UCAVs at close quarters, plays a decisive role on the aerial battlefields. With the development of artificial intelligence, WVR engagement progressively advances toward intelligent and autonomous modes. However, autonomous WVR engagement policy learning is hindered by challenges such as weak exploration capabilities, low learning efficiency, and unrealistic simulated environments. To overcome these challenges, we propose a novel imitative reinforcement learning framework, which efficiently leverages expert data while enabling autonomous exploration. The proposed framework not only enhances learning efficiency through expert imitation but also ensures adaptability to dynamic environments via autonomous exploration with reinforcement learning. Therefore, the proposed framework can learn a successful policy of “pursuit-lock-launch” for UCAVs. To support data-driven learning, we establish an environment based on the Harfang3D sandbox. The extensive experimental results indicate that the proposed framework excels in this multistage task and significantly outperforms state-of-the-art reinforcement learning and imitation learning methods. Thanks to the ability of imitating experts and autonomous exploration, our framework can quickly learn the critical knowledge in complex aerial combat tasks, achieving up to a 100% success rate and demonstrating excellent robustness.