Abstract
Two approaches are combined to improve the performance of modelling long wavelength holographic imaging (detection) of concealed objects. One approach is to design a multi-layer back propagation neural network that is able to reduce the effect of noise in a captured signal and results in a model as close as possible to the desired one. The other approach is to further process the captured signal by applying a modified covariance spectral estimation method to improve the resolution of the reconstructed image. Different concealing media and different values of signal to noise ratio are used to investigate the performance of such combination. It has been proved that the proposed model can provide an overall improvement in the imaging of concealed objects.
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