Fourier transform-based optimization of particle velocity estimation for noise reduction in tracking experiments

High-frequency noise, often caused by system jitter and environmental factors, can obscure the true motion of particles. This study presents a Fourier transform-based particle velocity optimization framework designed to improve the accuracy of velocity estimation in particle tracking experiments. High-frequency noise, often caused by system jitter and environmental factors, can obscure the true motion of particles. To address this, we propose an adaptive low-pass filtering approach where the cutoff frequency is optimized through a numerical search algorithm to minimize the error between the filtered velocity and the ground truth trajectory. Our results demonstrate that an optimal cutoff frequency of approximately 1 Hz offers the best balance between noise reduction and signal preservation. The framework is further enhanced by its adaptability to different experimental conditions, making it applicable to a wide range of particle tracking scenarios. This approach offers a more effective solution for overcoming noise-related challenges in particle tracking, providing a valuable tool for precise motion analysis in various scientific fields.