A Camera-Cooperative ISAC Framework for Multimodal Non-Cooperative UAVs Sensing

2026-05-23 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Software Development & Engineering · Depth: Expert, extended

Summary

The Camera-Cooperative ISAC (CC-ISAC) framework is proposed to enhance the detection and tracking of non-cooperative unmanned aerial vehicles (UAVs) within Integrated Sensing and Communication (ISAC) systems. This novel framework overcomes single-modal perception limitations and resource contention by integrating cameras for coarse airspace monitoring with ISAC for fine-grained, high-precision sensing. It features two key modules: the Vision-to-Echo Data Alignment (V2EDA) model, which aligns visual and echo-domain features using cross-attention, and the Multimodal Fusion-Based Estimation (MMFE) model, which robustly estimates UAV states by fusing historical and current multimodal data. Evaluated on the DeepSense6G dataset, CC-ISAC achieved an average 71% reduction in beam steering overhead and a 1.69–11.15% reduction in tracking overhead, while maintaining high angular estimation accuracy. This approach effectively frees substantial system resources for additional communication tasks.

Key takeaway

For AI Engineers developing ISAC systems for non-cooperative UAV surveillance, you should consider adopting a camera-cooperative multimodal sensing approach. This framework significantly reduces beam steering overhead by 71% and tracking overhead by up to 11.15%, freeing critical communication resources. Implementing V2EDA for visual-to-beamspace mapping and MMFE for robust multimodal fusion will enhance angular accuracy and system robustness, even under channel degradation or data loss, ensuring reliable long-term UAV perception.

Key insights

Multimodal sensing, combining camera and ISAC, significantly optimizes resource allocation for non-cooperative UAV detection and tracking.

Principles

• Multimodal sensing enhances accuracy and resource efficiency.
• Hierarchical task partitioning optimizes sensor roles.
• Cross-attention aligns heterogeneous sensor data.

Method

Cameras provide coarse UAV localization, guiding ISAC beam selection. V2EDA aligns visual-to-beamspace, while MMFE fuses historical multimodal data for robust tracking.

In practice

• Use YOLOv4 for initial visual UAV detection.
• Implement diffusive beam scanning for efficient search.
• Employ modality-aware imputation for data loss.

Topics

• Integrated Sensing and Communication
• UAV Detection
• Multimodal Sensor Fusion
• Beam Steering
• Beam Tracking
• DeepSense6G

Best for: Research Scientist, Computer Vision Engineer, AI Scientist, AI Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.