Jonas Gedschold

and 4 more

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 .

Sebastian Semper

and 4 more

Multidimensional channel sounding measures the geometrical structure of mobile radio propagation. The parameters of a multipath data model in terms of directions, time-of-flight and Doppler shift are estimated from observations in frequency, time and space. A maximum likelihood estimation framework allows joint high-resolution in all dimensions. The prerequisite for this is an appropriate parametric data model that represents the multipath propagation correctly. At the same time, a device data model is necessary that typically results from calibration measurements. The used model should be as simple as possible since its structure has a considerable effect on the estimation effort. For instance, the inherent effort in parameter search is reduced if the influence of the parameters is kept orthogonal. Therefore, the data model is characterized by several approximations. The most important is the “narrowband assumption” which assumes a low relative bandwidth and also avoids considering any frequency response in magnitude and phase. We extend the well-known multidimensional \gls{rimax} parameter estimation framework by including proper frequency responses. The advantage reveals most clearly with high bandwidth in the mmWave and sub-THz range. It allows for a more realistic modeling of antenna arrays. It breaks with the usual narrowband model and allows a better modeling of mutual coupling and time delay effects. If the interacting object extends over several delay bins (hence an extended target in radar terminology) we propose a model that assigns a short delay spread, respectively a frequency response to the propagation path that associates itto the respective object. We verify the validity of the device model by numerical experiments on simulated and measured antenna data and compare it to a state-of-the-art method. Additionally, we use synthetic data based on raytracing results and measurements both ranging from \SI{27}{\giga\hertz} up to \SI{33}{\giga\hertz} with known ground truth information and show that the proposed estimator not based on the narrowband assumption delivers better performance for higher relative bandwidths than the conventional \gls{rimax} implementation.