Optimal linear filtering for weak target detection based on dyadic contrast function analysis in RFT

In this study, a forward model for radio frequency tomography (RFT) is improved to remove the effect of strong sidelobes from dominant scatterers in the region of interest. This approach uses a ‘suppression algorithm (SA)’ to remove the effect of strong sidelobes on weak targets. SA is used to remove the effect of these strong sidelobes using the information from the dyadic contrast function (DCF). DCF is analysed in order to remove the effects of strong sidelobes generated by dominant cells in the measurement domain. The eigenvalues and eigenvectors for dominant cells are obtained to remodel the strong cells as a secondary source in the measurement scene. Furthermore, to simplify the inversion problem in the new forward model, iterative reconstruction algorithms is considered. Subsurface multiplicative algebraic reconstruction technique as additive technique is proposed to solve the new forward model RFT with less computing power and memory. The presented algorithm has been verified using simulated RFT data, generated by the computational electromagnetic software FEKO, for regular and irregular targets scenarios. The proposed research shows that using information from the DCF it is possible to obtain high quality imagery of buried weak targets in RFT.