Detection of concealed objects in passive millimeter wave imaging based on CS theory

This paper reports development of a passive millimeter wave imaging system for detection of concealed objects using sparse characterization and reconstruction algorithm based on CS theory. By measuring the radiometric temperatures of different objects, passive millimeter wave imaging can acquire images of objects concealed underneath clothing. In this system a millimeter wave antenna controlled by revolving table receives the passive millimeter signal from target object. As passive millimeter wave images have poor contrast and low signal to noise ratio, traditional segmentation algorithms are unable to detect concealed objects, so this system uses a method which transforms the image signal by a non-adaptive base matrix. The high-dimensional signal will be transformed to a lower dimensional space, and then solve an optimization problem for the highest sparsity of the signal through the gradient projection. We present experimental results of 94GHz passive millimeter wave images.