The paper delivers the first comprehensive survey of MIMO OFDM-based ISAC for low-altitude non-cooperative UAV surveillance, covering system modeling, detection and tracking, identification methods, experimental validations, open challenges, and future directions toward 5G-A and 6G.
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4 Pith papers cite this work. Polarity classification is still indexing.
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TERDNet introduces a transformer-encoder recurrent-decoder architecture for scene change detection that outperforms prior models on public benchmarks.
MUSE applies Mamba sequential modeling to produce real-time uncertainty estimates for visual-inertial state estimation from asynchronous multimodal sensors.
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.
citing papers explorer
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MIMO OFDM-Enabled ISAC for Low-Altitude Non-Cooperative UAV Surveillance: A Survey
The paper delivers the first comprehensive survey of MIMO OFDM-based ISAC for low-altitude non-cooperative UAV surveillance, covering system modeling, detection and tracking, identification methods, experimental validations, open challenges, and future directions toward 5G-A and 6G.
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TERDNet: Transformer Encoder-Recurrent Decoder Network for Scene Change Detection
TERDNet introduces a transformer-encoder recurrent-decoder architecture for scene change detection that outperforms prior models on public benchmarks.
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MUSE: Multimodal Uncertainty Quantification of State Estimation
MUSE applies Mamba sequential modeling to produce real-time uncertainty estimates for visual-inertial state estimation from asynchronous multimodal sensors.
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Learning-Based Spectrum Cartography in Low Earth Orbit Satellite Networks: An Overview
The paper overviews attention-based learning methods for spectrum cartography in LEO satellite networks to enable adaptive fusion of heterogeneous measurements for inference and resource allocation.