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In a military scenario, targets release decoy projectiles to achieve cover, then escape, evading the attack range of the guidance system. Therefore, recognizing target behaviors such as releasing decoys, escaping, and maneuvering, and locating the spatial position and behavior of the target can provide richer information for the precise targeting of the guidance system. Based on the behaviors of both the target and decoy, a behavioraware anti-interference algorithm is proposed. The core idea of this method is to link event recognition with spatio-temporal action localization tasks. It extracts spatio-temporal features of consecutive frames through a 3D backbone network and utilizes a 2D backbone network with a feature pyramid to extract spatial features at different levels of key frames for behavior detection at different scales. Additionally, a context extraction network is introduced in the key frames to achieve global context awareness, improving the accuracy of event recognition. Taking into account the correlation between spatial information, temporal information, and contextual information, this paper introduces channel attention mechanisms to fuse various feature components. Simultaneously, a non-local attention module is incorporated into the 3D backbone network to enhance the understanding of global content and the connection between different frames. Experimental results demonstrate that the proposed algorithm outperforms other algorithms, achieving a frame mean Average Precision (mAP) of 92.69% on our anti-interference dataset,enabling better resistance to interference.
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