Download pdf
Fourier transform-based optimization of particle velocity estimation for noise reduction in tracking experiments
- 1 University of Science and Technology of China
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
particle tracking, velocity estimation, Fourier transform, signal optimization
[1]. Wang, B., Zhang, X. X., Sun, Y. J., Qu, Z. W., & Li, X. C. (2019). The transport phenomenon of inertia Brownian particles in a periodic potential with non-Gaussian noise. Modern Physics Letters B, 33(2), 1950004. https://doi.org/10.1142/s0217984919500040
[2]. Zimon, M. J., Reese, J. M., & Emerson, D. R. (2016). A novel coupling of noise reduction algorithms for particle flow simulations. Journal of Computational Physics, 321, 169-190. https://doi.org/10.1016/j.jcp.2016.05.049
[3]. Ooms, T., Koek, W., Braat, J., & Westerweel, J. (2006). Optimizing Fourier filtering for digital holographic particle image velocimetry. Measurement Science and Technology, 17(2), 304-312. https://doi.org/10.1088/0957-0233/17/2/011
[4]. Pinto, M. C., Ameres, J., Kormann, K., & Sonnendrücker, E. (2024). On Variational Fourier Particle Methods. Journal of Scientific Computing, 101(3), 68. https://doi.org/10.1007/s10915-024-02708-w
[5]. Chávez, G. M., Castillo-Rivera, F., Montenegro-Ríos, J. A., Borselli, L., Rodríguez-Sedano, L. A., & Sarocchi, D. (2020). Fourier Shape Analysis, FSA: Freeware for quantitative study of particle morphology. Journal of Volcanology and Geothermal Research, 404, 107008. https://doi.org/10.1016/j.jvolgeores.2020.107008
[6]. Durak, L., & Aldirmaz, S. (2010). Adaptive fractional Fourier domain filtering. Signal Processing, 90(4), 1188-1196. https://doi.org/10.1016/j.sigpro.2009.10.002
[7]. Daum, F., & Huang, J. (2013). Fourier transform particle flow for nonlinear filters. In Conference on Signal Processing, Sensor Fusion, and Target Recognition XXII (Vol. 8745). Spie-Int Soc Optical Engineering. https://doi.org/10.1117/12.2001666
[8]. Henriques, J. F., Caseiro, R., Martins, P., & Batista, J. (2015). High-Speed Tracking with Kernelized Correlation Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3), 583-596. https://doi.org/10.1109/tpami.2014.2345390
[9]. Zhang, H., Zhou, S., & Feng, S. (2016). Gaussian Particle Flow Filter. Acta Electronica Sinica, 44(4), 795-803.
[10]. Kemao, Q. (2008). A simple phase unwrapping approach based on filtering by windowed Fourier transform: A note on the threshold selection. Optics and Laser Technology, 40(8), 1091-1098. https://doi.org/10.1016/j.optlastec.2008.03.005
Cite this article
Yan,C. (2025). Fourier transform-based optimization of particle velocity estimation for noise reduction in tracking experiments. Advances in Engineering Innovation,16(3),15-23.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title:
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).