Estimating Electrically Small Targets Using Equivalent Dipoles and Sparse Processing

• Published in: IEEE transactions on antennas and propagation Vol. 69; no. 7; pp. 4123 - 4135
• Main Authors: Stevanovic, Marija Nikolic, Dinkic, Jelena Lj, Djordjevic, Antonije R.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-07-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Dipole antennas; Electric fields; Electromagnetic (EM) scattering; Electromagnetic fields; Equivalence; Estimation; Image reconstruction; Inverse problems; Magnetic dipoles; Magnetic field measurement; Magnetic moments; Magnetic resonance imaging; microwave imaging (MWI); Numerical stability; Regularization; sparse processing; Transmission line measurements
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2020.3045608

We develop an algorithm for localizing and estimating electrically small targets using sparse processing framework and vector electric-field measurements. To model the sources of electromagnetic field, we use equivalent electric and magnetic dipoles, assuming that only a few of them are sufficient for accurate source representation. To find the locations and complex amplitudes of the equivalent dipoles, we apply the <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula> regularization, which mitigates the inherent ill-posedness of the inverse problem. The method includes a normalization scheme which harmonizes imbalances in the system matrix and, consequently, improves the numerical stability of the method. In addition, we develop an algorithm for finding the optimal value of the regularization coefficient, based on the L-curve approach. The performance of the algorithm has been extensively tested using experimental data collected over a wide frequency bandwidth.