Reiß, SarahSarahReißKnopp, TobiasTobiasKnoppAckers, JustinJustinAckersFaltinath, JonasJonasFaltinathMohn, FabianFabianMohnBoberg, MarijaMarijaBobergTimm, NoraNoraTimmMöddel, MartinMartinMöddel2026-02-232026-02-232026-02-23https://hdl.handle.net/11420/61653The TUHH Open Research (TORE) repository entails the experimental data of the paper "Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators". Magneto-mechanical resonator (MMR) represent a recently proposed type of passive sensor that enables the estimation of its pose as well as sensing other parameters in its environment. The working principle of MMRs entails an excitation of the sensors by oscillating magnetic fields, followed by a readout process facilitated by inductive receiver coils. The sensing technology relies on real-time parameter estimation. This encompasses the solution of a nonlinear inverse problem, with the induced signals and a suitable forward model as inputs. The aim of this paper is twofold: first, to introduce a reference model and simplified models for the MMR dynamics and inductive readout, and second, to provide robust and real-time capable methods to estimate the model parameters. The effectiveness of the presented methods is evaluated in terms of their real-time potential, precision, and accuracy. All presented methods demonstrate the capacity to estimate the measured signal, with the simplified methods reducing the corresponding parameter estimation time by up to two orders of magnitude at the expense of less than 4 % deviation for large maximum deflection angles.https://creativecommons.org/publicdomain/zero/1.0/magneto-mechanical resonatormodel-based sensingonline parameter estimationinverse problemmulti-component signalTechnology::629: Other Branches::629.8: Control and Feedback Control SystemsExperimental Datahttps://doi.org/10.15480/882.1674210.15480/882.16742Knopp, TobiasTobiasKnopp10.48550/arXiv.2602.19965