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UAV-Based Target Detection, Tracking, and Prediction: A Survey From Classical Models to AI-RAN Intelligence
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DOI：10.1109/access.2026.3718358.png)
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Abstract
En 中文
Recent advances in unmanned aerial vehicle (UAV)-based target detection and tracking increasingly rely on model-based techniques such as Kalman filtering (KF) and particle filtering (PF), as well as learning-driven approaches including deep learning and reinforcement learning. Despite these developments, existing UAV systems continue to face major challenges arising from increasing target densities, complex terrain, dynamic wireless conditions, communication limitations, and restricted onboard computational and energy resources. These constraints significantly affect tracking accuracy, real-time responsiveness, and service reliability, particularly in resource-constrained and rapidly changing environments. To address these challenges, this survey presents a systematic and comprehensive review of UAV-based target detection, tracking, and prediction methods, spanning classical estimation models, deep learning frameworks, reinforcement learning strategies, and cooperative multi-UAV intelligence. The survey further emphasizes the integration of UAV-assisted edge computing with the Open Radio Access Network (O-RAN) framework, where the RAN Intelligent Controller (RIC), together with xApps and rApps, enables scalable, low-latency, and adaptive communication optimization between aerial and terrestrial nodes. Building on this foundation, the survey introduces an Artificial Intelligence-Driven Radio Access Network (AI-RAN)-enhanced conceptual framework that combines particle filtering, Q-Learning (QL) control, and AI-driven RAN optimization to enable joint communication, computation, and control. The proposed architectural perspective demonstrates how multi-modal sensor fusion and distributed edge intelligence can jointly improve tracking robustness, responsiveness, and energy efficiency. Finally, the survey highlights open challenges and future research directions toward fully autonomous, scalable, and network-aware UAV tracking systems for emerging 6G and edge-AI environments.
Keywords:
Unmanned aerial vehicle (UAV) systems
target detection
tracking
motion prediction
sensor fusion
Kalman filtering
particle filtering
reinforcement learning
deep learning
open radio access network (RAN)
RAN intelligent controller
AI-driven RAN
UAV swarm coordination
Journal
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3.6
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9.7W
Citations:
29.4W
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Cited Papers