Dynamic image reconstruction in electrical capacitance tomography using particle filters

Electrical capacitance tomography (ECT) is a non-invasive imaging technique which is used to acquire information about the spatial material distribution of inaccessible objects in order to monitor industrial processes such as two-phase flow fields in the oil industry. In this contribution, the application of a particle filter (PF) to solve the inverse ECT problem is proposed. PFs offer the possibility to tackle time varying objects during the reconstruction task given uncertain measurements. By implementing a filter bank, the merging and splitting of bubbles which is frequently encountered in dynamic flow processes is taken into account. The novelty of the proposed sensor lies in the multimodal state-space representation that includes object motion and dynamic contours and is able to cover a variety of physically meaningful transitions. The signal processing algorithm is validated using measured data