Optimal Estimation of Nanoparticle Motion in Optical Tweezer and Dielectrophoretic Traps Using Kalman Filters

Accurate tracking of microparticle motion in optical tweezers and dielectrophoretic (DEP) traps is essential for advancing applications in nanotechnology, microfluidics, and biomedical systems. However, challenges such as stiff system dynamics, measurement noise, and fluid-induced variability complicate reliable state estimation. This study presents a Kalman filtering framework designed to estimate the position and velocity of microparticles confined in optical and DEP traps. To address computational constraints, both full-order and reduced-order models, including constant-velocity approximations and scaled dynamics, are explored. Filter consistency is evaluated using ±3σ confidence bounds, and estimation performance is validated against true simulated and experimental trajectories. The framework demonstrates accurate and robust estimation under both transient and steady-state conditions. Overall, the proposed approach provides a reliable foundation for real-time sensing and control of microparticle behavior.