Title: Spectral Domain Neural Reconstruction for Passband FMCW Radars
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Implicit Neural Representation, Neural Volumetric Reconstruction, Frequency Modulated Continuous Wave, mmWave radar

Abstract: We present SpINR, a novel neural framework for high-fidelity volumetric reconstruction using Frequency-Modulated Continuous-Wave (FMCW) radar. Traditional radar imaging techniques, such as backprojection, often assume ideal signal models and require dense aperture sampling, leading to limitations in resolution and generalization. Our core contribution is a fully differentiable frequency-domain forward model that captures the complex radar response using closed-form synthesis, paired with an implicit neural representation (INR) for continuous volumetric scene modeling. Unlike time-domain baselines, SpINR directly supervises the complex frequency spectrum, preserving spectral fidelity while drastically reducing computational overhead. Additionally, we introduce sparsity and smoothness regularization to disambiguate sub-bin ambiguities that arise at high carrier frequencies and fine range resolutions. Experimental results show that SpINR significantly outperforms both classical and learning-based baselines, with a 52.6\% improvement in reconstruction quality and 32\% improvement in latency.