Millimeter-wave (mmWave) imaging offers a promising, non-ionizing approach for biomedical diagnostics, yet its performance is constrained by the inherent trade-off between penetration depth and range resolution. This research introduces a novel computational framework designed to overcome this limitation by synthesizing a virtual ultra-wideband (UWB) aperture from sparse and non-contiguous spectral data. Building upon my Master’s work in MIMO radar array processing and direction-of-arrival (DoA) estimation, the study extends the concept of array extrapolation from the spatial domain into the spectral domain. The proposed framework integrates data from two complementary frequency bands: a low-frequency “scout” signal (1–10 GHz) to enable deep tissue detection, and a high-frequency “surveyor” signal (80–100 GHz) to achieve fine-resolution imaging. Its key innovation lies in leveraging both classical signal processing methods and advanced deep learning models—such as Recurrent Neural Networks (RNNs) and Transformers—to perform intelligent bandwidth extrapolation across the spectral gap. This strategy enables high-fidelity imaging of millimeter-scale tissue anomalies at depths unattainable with conventional high-frequency systems. The project will culminate in the development of an open-source Python simulation platform, offering a significant advancement in computational sensing and non-invasive diagnostic technologies.

i have build a small simulator to understand the basic idea behind