Yixiang Luomei

Zhentao Yang, Yixiang Luomei, Zhuoyang Liu et al.

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.

Jiangtao Wei, Yixiang Luomei, Xu Zhang et al.

Simulating high-resolution Synthetic Aperture Radar (SAR) images in complex scenes has consistently presented a significant research challenge. The development of a microwave-domain surface scattering model and its reversibility are poised to play a pivotal role in enhancing the authenticity of SAR image simulations and facilitating the reconstruction of target parameters. Drawing inspiration from the field of computer graphics, this paper proposes a surface microwave rendering model that comprehensively considers both Specular and Diffuse contributions. The model is analytically represented by the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the perturbation method (SPM). And SAR imaging is achieved through the synergistic combination of ray tracing and fast mapping projection techniques. Furthermore, a differentiable ray tracing (DRT) engine based on SAR images was constructed for CSVBSDF surface scattering parameter learning. Within this SAR image simulation engine, the use of differentiable reverse ray tracing enables the rapid estimation of parameter gradients from SAR images. The effectiveness of this approach has been validated through simulations and comparisons with real SAR images. By learning the surface scattering parameters, substantial enhancements in SAR image simulation performance under various observation conditions have been demonstrated.