Rathi, VamikaVamikaRathiSehar, FatimaFatimaSeharSommer, Finn LucaFinn LucaSommerGötschel, SebastianSebastianGötschelSteuwe, EikeEikeSteuweKameke, Alexandra vonAlexandra vonKamekeRuprecht, DanielDanielRuprecht2026-06-122026-06-122026-06-11arXiv: 2606.13099 (2026)https://hdl.handle.net/11420/63473Lagrangian sensors have shown promise to improve operator awareness of conditions inside a chemical reactor but three-dimensional tracking remains a mostly unsolved challenge. We explore a setup where in-silico sensors, based on a recently proposed real-world design, are tracked using data from an accelerometer and magnetometer available from a built-in inertial measurement unit. Filtering algorithms, using a bespoke dynamical model, are used to process these readings into position estimates. We compare tracking performance of an extended Kalman filter, a particle filter and the unscented Kalman filter implemented in the pykalman library. Our numerical experiments track in-silico particles moving in an analytically given three dimensional vortex as well as in the experimentally measured flow-field of a lab-scale stirred tank reactor. Using the Maxey-Riley-Gatignol equations for the movement of inertial particles as ground-truth, we demonstrate that trajectories can be reconstructed from noisy synthetic data with errors below 10%.enhttps://creativecommons.org/licenses/by/4.0/math.NATechnology::660: Chemistry; Chemical Engineering::660.2: Chemical EngineeringPreprinthttps://doi.org/10.15480/882.1730610.48550/arXiv.2606.1309910.15480/882.173062606.1309910.5281/zenodo.20629013