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This paper introduces ZoomSpec, a novel wideband spectrum sensing framework that combines signal processing principles with deep learning to improve narrowband visibility. ZoomSpec uses a Log-Space STFT (LS-STFT) to enhance narrowband structures and an Adaptive Heterodyne Low-Pass (AHLP) module for interference filtering and signal purification. The framework achieves state-of-the-art performance on the SpaceNet dataset, demonstrating superior stability across diverse modulation bandwidths with 78.1 mAP@0.5:0.95.
By explicitly modeling signal processing constraints, ZoomSpec achieves state-of-the-art wideband spectrum sensing, outperforming existing deep learning methods that treat spectrograms as natural images.
Wideband spectrum sensing for low-altitude monitoring is critical yet challenging due to heterogeneous protocols,large bandwidths, and non-stationary SNR. Existing data-driven approaches treat spectrograms as natural images,suffering from domain mismatch: they neglect time-frequency resolution constraints and spectral leakage, leading topoor narrowband visibility. This paper proposes ZoomSpec, a physics-guided coarse-to-fine framework integrating signal processing priors with deep learning. We introduce a Log-Space STFT (LS-STFT) to overcome the geometric bottleneck of linear spectrograms, sharpening narrowband structures while maintaining constant relative resolution. A lightweight Coarse Proposal Net (CPN) rapidly screens the full band. To bridge coarse detection and fine recognition, we design an Adaptive Heterodyne Low-Pass (AHLP) module that executes center-frequency aligning, bandwidth-matched filtering, and safe decimation, purifying signals of out-of-band interference. A Fine Recognition Net (FRN) fuses purified time-domain I/Q with spectral magnitude via dual-domain attention to jointly refine temporal boundaries and modulation classification. Evaluations on the SpaceNet real-world dataset demonstrate state-of-the-art 78.1 mAP@0.5:0.95, surpassing existing leaderboard systems with superior stability across diverse modulation bandwidths.
