The Impact of Phase Information on the Imaging Performance of the Extended Phaseless Rytov Approximation

Abstract

The extended phaseless Rytov approximation for lossy media (xPRA-LM) has been shown to significantly extend the validity range of the Rytov approximation. Due to its extended validity range, xPRA-LM can be useful for large-scale RF imaging applications such as Wi-Fi based indoor imaging. However xPRA-LM is formulated for datasets with phaseless data only. In this work, we investigate the extended phaseless Rytov approximation when phase is included and we refer to this formulation as the extended full-data Rytov approximation for low-loss media (xFRA-LM). Specific contributions of this work include the formulation of xFRA-LM, development of a Cramér-Rao Bound for xFRA-LM and also providing numerical comparisons with xPRA-LM. The development of xFDA-LM also opens a pathway for the extension of radio tomographic imaging (RTI) to include phase and this is also discussed. The results show that the Bayesian Cramér-Rao Bound for xFRA-LM is at least half that of the bound for xPRA-LM demonstrating the advantages of including phase. Furthermore, simulation results for reconstruction quality show that xFRA-LM is significantly enhanced as compared to xPRA-LM, in terms of validity range, imaging accuracy and imaging resolution.