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

arXiv: https://arxiv.org/abs/2605.22090 · HTML: https://mlsanity.com/paper/2605.22090/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 44 references that are in the map.
- First posted on arXiv 21 May 2026.

## Builds on (most cited first, 21 of 21)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [Integrated Sensing and Communications: Towards Dual-functional Wireless Networks for 6G and Beyond](https://mlsanity.com/paper/2108.07165/) (2021)
- [CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception](https://mlsanity.com/paper/2304.00670/) (2023)
- [Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review](https://mlsanity.com/paper/2304.10410/) (2023)
- [DeepSense 6G: A Large-Scale Real-World Multi-Modal Sensing and Communication Dataset](https://mlsanity.com/paper/2211.09769/) (2022)
- [Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism](https://mlsanity.com/paper/2309.11331/) (2023)
- [Real-Time Drone Detection and Tracking With Visible, Thermal and Acoustic Sensors](https://mlsanity.com/paper/2007.07396/) (2020)
- [Computer Vision Aided mmWave Beam Alignment in V2X Communications](https://mlsanity.com/paper/2207.11409/) (2022)
- [Intelligent Multi-Modal Sensing-Communication Integration: Synesthesia of Machines](https://mlsanity.com/paper/2306.14143/) (2023)
- [ASF-YOLO: A Novel YOLO Model with Attentional Scale Sequence Fusion for Cell Instance Segmentation](https://mlsanity.com/paper/2312.06458/) (2023)
- [Radar and Camera Fusion for Object Detection and Tracking: A Comprehensive Survey](https://mlsanity.com/paper/2410.19872/) (2024)
- [Initial Access in 5G mm-Wave Cellular Networks](https://mlsanity.com/paper/1602.07731/) (2016)
- [Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion](https://mlsanity.com/paper/2312.11008/) (2023)
- [Communication and Control in Collaborative UAVs: Recent Advances and Future Trends](https://mlsanity.com/paper/2302.12175/) (2023)
- [SIMAC: A Semantic-Driven Integrated Multimodal Sensing And Communication Framework](https://mlsanity.com/paper/2503.08726/) (2025)
- [Environment Semantic Communication: Enabling Distributed Sensing Aided Networks](https://mlsanity.com/paper/2402.14766/) (2024)
- [Ubiquitous Acoustic Sensing on Commodity IoT Devices: A Survey](https://mlsanity.com/paper/1901.03450/) (2019)
- [Vehicle Cameras Guide mmWave Beams: Approach and Real-World V2V Demonstration](https://mlsanity.com/paper/2308.10362/) (2023)
- [Large Language Model-Driven Distributed Integrated Multimodal Sensing and Semantic Communications](https://mlsanity.com/paper/2505.18194/) (2025)
- [Extended Kalman Filter Beam Tracking for Millimeter Wave Vehicular Communications](https://mlsanity.com/paper/1911.01638/) (2019)

## Abstract (arXiv)

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.