PCB current identification based on near-field measurements using preconditioning and regularization
Radiated electromagnetic fields from a PCB can be estimated when the source current distribution is known. From a measured near-field distribution, the PCB source current distribution can be found. Accuracy depends on the measurement method and its limitations, the radiation model and the choice of the observation area. Many known methods are based on optimization algorithms for inverse problems that vary a set of elementary radiation sources and create a radiation model. However, apart from the time-consuming optimization process, such methods find one possible solution for a near-field distribution. As this distribution might not reflect the real current distribution, accuracy outside of near-field scan area can be low. Furthermore numerical problems can often be observed. Solving the given inverse problem with a system of linear equations and complex near-field data it can be very sensitive to noise. Regularization methods and an adjusted preconditioning can increase the accuracy. In this paper, an improved radiation model creation approach based on complex near-field data is presented. This approach is based on regularization methods and extended by current estimations from near-field data. Preconditioning is done considering some physical properties of the PCB and its possible current paths. Accuracy and stability of the method are investigated in the presence of noisy data.