Fuzzy Kalman Filter for 3D Lagrangian Particle Tracking Using Multi-scale Bubble Detection

AbstractA critically important quantity of energy is required nowadays to heat and ventilate our buildings. In order to reduce this energy demand, we need to characterize the convective indoor air movements in large scale spaces like building rooms. 3D Lagrangian Particle Tracking is used here; hence a feedback schema has been designed, it applies multi-view information to correct uncertainties in particle positioning. Moreover, a multi-scale based approach has been developed and tested with experimental and synthetic datasets. The method shows good robustness and efficiency against different kinds of noise and allows a generally applicable algorithm thanks to the use of automatic scale selection. On the other hand, the particle tracking is another challenging problem. Therefore, a new tracking algorithm based on fuzzy Kalman filtering is proposed. The Kalman filter is used to optimally estimate the new position of the particles based on their actual position. A quantitative comparison of the performances of this technique and other commonly used tracking algorithms allows validating the method