Abstract:The detection of non-cooperative unmanned aerial vehicles (UAVs) presents significant challenges for Integrated Sensing and Communication (ISAC) systems due to the inherent limitations of single-modal perception and the competition for shared communication and sensing resources. To address these challenges, this paper proposes a novel Camera-Cooperative ISAC (CC-ISAC) framework that employs multimodal sensing to enable efficient UAV beam steering and tracking. The proposed framework employs cameras for coarse-grained airspace monitoring and utilizes ISAC for fine-grained, high-precision sensing, forming a complementary perception loop that enhances both sensing accuracy and resource efficiency. Within this framework, two key modules are developed: (1) a Vision-to-Echo Data Alignment (V2EDA) model that aligns visual and echo-domain features through cross-attention mechanisms, and (2) a Multimodal Fusion-Based Estimation (MMFE) model that integrates historical multimodal data with current observations for robust state estimation. Extensive evaluations conducted on the DeepSense 6G dataset demonstrate that the proposed framework achieves an average reduction of 71% in beam steering overhead and 1.69-11.15% in tracking overhead while maintaining high angular estimation accuracy. The CC-ISAC framework effectively mitigates resource contention between sensing and communication, enabling reliable UAV surveillance while freeing substantial system resources for additional communication tasks, thereby representing a practical advancement in ISAC system design.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.22090 [cs.AI] |
| (or arXiv:2605.22090v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22090 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenfeng Wu [view email]
[v1]
Thu, 21 May 2026 07:31:51 UTC (4,357 KB)