Air-to-ground (A2G) channel modeling supports low-altitude wireless networks for unmanned aerial vehicle (UAV) services in 5G/6G systems. Existing data-driven models are often benchmarked against weak empirical baselines, decoupled from the underlying measurement campaigns, and prone to overfitting on small UAV dataset...
Fu-Yuan Ma, Wen-Rui Ding, Shu-Tong Wang et al.· Drones· 0 citations
Direction-of-arrival (DOA) estimation under low signal-to-noise ratio (SNR) and finite-snapshot conditions remains challenging in array signal processing. Under such conditions, the sample covariance matrix may deviate substantially from the true covariance matrix. This deviation degrades the estimation performance of...
Intelligent spoofing poses a critical threat to unmanned aerial vehicle (UAV) communications by corrupting synchronization and channel estimation while evading power-based detection. This paper presents IDTD-RL, a risk-sensitive reinforcement learning framework for adaptive anti-spoofing defense in OFDM-based UAV syste...
Hang Zhang, Wen-Rui Ding, Yu-Feng Wang et al.· IEEE Transactions on Informa...· 1 citation
Direction-of-arrival (DOA) estimation in complex electromagnetic environments plays a critical role in target detection and localization. However, achieving accurate estimation remains a challenge under adverse and non-ideal conditions. We propose a novel self-supervised learning and adversarial augmentation complex-va...
GAAT (Geometry-Aware Alignment Transformer), an alignment-first pretrained model that estimates local correspondence reliability before cross-modal interaction, is proposed and introduced, establishing GAAT as a state-of-the-art multimodal foundation model for UAV perception.
Jing-Pu Yang, Deming Tang, Yi-Lin Sun et al.· 1 citation
The Hash-Atlas network is proposed, which reformulates 3D scene editing as operations on 2D atlas images, thereby achieving a workflow decoupling of the 2D editing and 3D reconstruction processes.
Shuangkang Fang, Yu-Feng Wang, Yi-Hsuan Tsai et al.· 1 citation
SAP-Nav is presented, a fully online, zero-shot framework that supports both hierarchical and standard category-level OVON without task-specific training or precomputed scene maps and is designed for hierarchical OVON without task-specific training or precomputed scene maps.
Xuetong Pei, Jian Liu, Vidura Munasinghe et al.· 1 citation
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