Department Publications

This paper discusses advances related to the integration of future mobile electronic THz systems. Without claiming to provide a comprehensive review of this surging research area, the authors gathered research on selected topics that are expected to be of relevance for the future exploration of components for practical mobile THz imaging and sensing applications. First, a brief technology review of integrated mobile THz components is given. Advances in III-V technology, silicon technology, and resonant-tunneling diodes (RTD) are discussed. Based on an RTD source and a SiGe-HBT direct detector, low-cost and compact computed tomography is presented for volumetric continuous-wave imaging at around 300 GHz. Moreover, aspects of system integration of mobile THz MIMO radars are discussed. Thereby, a novel phase-locked loop concept utilizing a high-stability yttrium-iron-garnet-tuned oscillator to …

Estimating parameters and properties of various materials without causing damage to the material under test (MUT) is important in many applications. Thus, in this letter, we address MUT's parameter estimation by wireless sensing. Here, the precision of the estimation depends on the accurate estimation of the properties of the reflected signal from the MUT (e.g., number of reflections, their amplitudes, and time delays). For a layered MUT, there are multiple reflections, and due to the limited bandwidth at the receiver, these reflections superimpose with each other. Since the number of reflections coming from the MUT is limited, we utilize sparse signal processing (SSP) to decompose the reflected signal. In SSP, a so called dictionary is required to obtain a sparse representation of the signal. Here, instead of a fixed dictionary, an iterative dictionary-update technique is proposed to improve the estimation of the reflected …

We present a compressive sensing based defect detection by multiple input multiple output (MIMO) wireless radar. Here, defects are inside a layered material structure, therefore, due to reflections from the surface of the layered material structure the defect detection is challenging. By utilizing a low-rank nature of the reflections of the layered material structure and sparse nature of the defects, we propose a method based on rank minimization and sparse recovery. To improve the accuracy in the recovery of low-rank and sparse components, we propose a non-convex approach based on the iteratively reweighted nuclear norm and iteratively reweighted norm algorithm. Our numerical results show that the proposed method is able to demix and recover the signalling responses of the defects and layered structure successfully from substantially reduced number of observations. Further, the proposed approach outperforms the state-of-the-art clutter reduction approaches