Detection of low-metallic content landmines based on electromagnetic induction model : doctoral thesis - Ambruš, Davorin (D. Ambruš ; Fakultet elektrotehnike i računarstva, 2019)
This thesis presents an attempt to improve the existing methods of close-in detection of landmines using electromagnetic induction (EMI) sensing. In the introductory part, an analytical forward model of a scanning EMI sensor for metallic target characterization based on the induced magnetic dipole model is derived from the governing EMI equations. Three different static inversion methods for the estimation of induced dipole model parameters from EMI and sensor positional data are analyzed; the method based on nonlinear least squares (NLS) approach, the quasi-numerical HAP method featuring the modified NSMS model, and the metaheuristic method. Inversion performance of each method is evaluated on a synthetic data set, for selected metallic targets and representative search head geometries. A novel approach to search head's pose tracking based on the measurement of its own primary magnetic field and the application of the extended Kalman filter is presented. Following a similar methodology, a new concept of dynamic dipole inversion that avoids the need for sensor positional data is proposed. The effects of background interferences on a response of an EMI sensor and the methods for their compensation are also analyzed. A novel concept for robust estimation of target's shape, based on temporal information stored in the eigenvalues of the estimated magnetic polarizability tensor, is introduced. Finally, all of the analyzed modeling, inversion, tracking and compensation approaches are experimentally validated on several sensing platforms in a laboratory and field environment. KEYWORDS: metal detection, metal characterization, electromagnetic induction, induced magnetic dipole, magnetic polarizability tensor, nonlinear least squares, magnetic tracking.