Multi-sensor data fusion between radio tomographic imaging and noise radar

Abstract

Radio tomographic imaging and noise radar are two proven surveillance technologies. The novelty of fusing data from radio tomographic imaging and noise radar is achieved with the derivation of a fusion technique utilising Tikhonov regularisation. Analysing the results of the Tikhonov influenced techniques reveals an average 43-47% error decrease in target centroid location, a 13-19% size decreases in target pixel dispersion and a 6-41% improvement in an ideal solution comparison. Results provide the radio tomographic imaging and noise radar communities a proof of concept for the fusion of data from two disparate sensor technologies.