Planar Near-Field Electric Field Sensor Array Applications Facilitated by Neural Networks

Electrical capacitance tomography is employed for various process tomography applications, typically with circular imaging regions (e.g., to estimate fluid levels in plastic pipes). Typical state-of-the-art implementations focus on circular or cylindrical sensor arrays. In contrast, this research explores using a planar 2D array of electric-field sensors to image volumes composed of various dielectric materials. The array is designed to be used with very-low-frequency electric fields, which are desirable due to their ability to differentiate between various non-conducting objects. D-dot sensors (i.e., charge induction sensors) are used as the electric-field sensing element. In this research, imaging regions of interest are modeled as a composition of (25 cm)3 voxels of dielectric material with randomized relative permittivities. Neural networks are utilized as the inversion algorithm to map measured E-field distortions to the voxels' relative permittivities. Three applications are explored in a simulated environment: 1) predicting relative permittivities of the entire (pseudo-3D) imaging region from one measurement of electric-field distortions (modeled in free space), 2) imaging regions arbitrarily large (in two dimensions) using the planar array as an imaging kernel, and 3) repeating application (1) in a model of a practical, real-world imaging scenario both with and without interfering material. Application (3) is performed with a real-world experimental setup using a room-sized 'E-field Cage' meant to generate a uniform electric field. This work showcases a new electric-field imaging modality using a planar 2D D-dot sensor array paired with a DNN-based inversion algorithm.