Improve Signals Discrimination for an Indoor Environment Using the Sparse Deconvolution Algorithm

Ray tracing is a useful tool to model the spatial and temporal propagation parameters of electromagnetic radiation. The model can normally improve by the channel sounding. However, discrepancies may arise between the channel sounding and ray-tracing model due to it needs a wide bandwidth to differentiate the incoming signals, also the antenna radiation pattern may change dramatically over this wide frequency span. To overcome this limitation, in this paper, the sparse deconvolution will be used to test and improve data measurements from real environments. Good results are obtained that shows that Cauchy-Gaussian model can differentiate between two different signals that has 0.5m distances offset for 200 MHz and 0.8m for 150 MHz theoretically. Then, comparison between the measured data using the basic Inverse Discrete Fourier Transform and the Cauchy-Gaussian model for two different scenarios shows that signals arrival in time cannot adequately determined using only Inverse Discrete Fourier Transform technique at 150 MHz, while on the contrary, the Cauchy-Gaussian model can still discriminate between them.