Developing channel models typically requires aggregating channel measurements and the corresponding extracted propagation parameters from different research institutions to form a sufficiently large data basis. However, uncertainties arising from limitations of the sounding hardware and algorithms may greatly impact the comparability between sounding results. Especially, (sub-) THz channel sounding is challenged by a potentially low SNR. At the same time, high Doppler shifts may occur due to the high carrier frequencies, limiting the time spans for coherent or incoherent data processing. Hence, the channel dynamics additionally restrict the processing gain for the SNR. In this paper, we address these challenges metrologically from several perspectives. First, we discuss methods of baseband waveform precoding to adapt it to the sounder hardware or the employed high-resolution estimation algorithms. This allows using available transmit power in an optimal sense. Second, the assessment of the sounder performance requires a traceable reference allowing tracing back measurements (or estimated propagation parameters) to a physical ground truth. Therefore, we propose and discuss an over-the-air artifact allowing a joint verification of delay and Doppler parameters in a multipath scenario. The evaluations of exemplary sub-THz measurements with a multicarrier-based sounder highlight the strong interplay between sounder hardware and estimation algorithms, especially when coping with the mutual interference of parameters from multiple propagation paths. Hence, a metrological assessment always requires considering the full processing pipeline from the unprocessed measurements up to the extracted propagation parameters.

In this publication, we analyze how the performance of propagation parameter estimation for THz channel sounding can be improved by the power spectrum design of a multicarrier waveform. To this end, we discuss the Fisher information of the propagation parameters and the corresponding deterministic Cramèr-Rao lower bound (CRB) as well as their relation to the carrier powers of the excitation signal. We use these quantities to design waveforms that improve range estimation. In practice, optimizing the power spectrum requires prior knowledge of the propagation scenario which is usually not available. Hence, we propose two solutions which we compare numerically to the classical approach of equal power distribution. The numerical evaluation shows that an optimized power distribution can improve the CRB comparable up to a 4 dB gain in SNR depending on whether knowledge about the scenario can be acquired in advance with an additional measurement. © 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This publication proposes a parametric data model and a gradient-based maximum likelihood estimator suitable for the description of delay-dispersive responses of multiple dynamic UWB-radar targets. The target responses are estimated jointly with the global target parameters range and velocity. The large relative bandwidth of UWB has consequences for model-based parameter estimation. On the one hand, the Doppler effect leads to a dispersive response in the Doppler spectrum and to a coupling of the target parameters which both need to be considered during modeling and estimation. On the other hand, the shape of an extended target results in a dispersive response in range which can be resolved by the radar resolution. We consider this extended response as a parameter of interest, e.g., for the purpose of target recognition. Hence, we propose an efficient description and estimation of it by an FIR structure only imposing a restriction on the target’s dispersiveness in range. We evaluate the approach on simulations, compare it to state of the art solutions and provide a validation on measurement data. © 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works .