Jin Yang

In this study, we propose a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average peak signal-to-noise ratio (PSNR) improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate fast fourier transform (FFT) methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions.

This paper proposes a novel pixel-level non-local similarity (PNS)-based reconstruction method for millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging. Unlike traditional compressed sensing (CS) methods, which rely on predefined sparse transforms and often introduce artifacts, our approach leverages structural redundancies in InSAR images through an enhanced sparse representation model with dynamically filtered coefficients. This design simultaneously preserves fine details and suppresses noise interference. Furthermore, an iterative refinement mechanism incorporates raw sampled data fidelity constraints, enhancing reconstruction accuracy. Simulation and physical experiments demonstrate that the proposed InSAR-PNS method significantly outperforms conventional techniques: it achieves a 1.93 dB average PSNR improvement over CS-based reconstruction while operating at reduced sampling ratios compared to Nyquist-rate FFT methods. The framework provides a practical and efficient solution for high-fidelity millimeter-wave InSAR imaging under sub-Nyquist sampling conditions.

This paper describes the design and realization of a novel Ka/W dual-band shared-aperture horn antenna employing the concept of mutual enhancement. The W-band antenna cells within the Ka-band antenna structure enhance the aperture utilization efficiency. The proposed design enables gain enhancement in the Ka-band horn antenna through incorporation of W-band horn antenna cells, while the W-band array antenna achieves low sidelobe levels by adopting a sparse array configuration. The W-band antenna consists of a radiation sparse array, an irregular interconnected waveguide-based network, and a power divider network. A mutually enhanced Ka/W dual-band shared-aperture horn antenna is fabricated and evaluated. The results reveal a gain improvement of 1.5 dB at 35 GHz compared to the original horn antenna. Moreover, the measured |S11|≤-10 dB bandwidth of the W-band array antenna spans over 16 GHz (ranging from 92 GHz to 108 GHz). At the central frequency, the maximal gain of the radiation pattern is approximately 20.8 dBi, with low sidelobe levels (in both the E-plane and the H-plane) of less than -19.0 dB.