Utilization of multiple polarization data for aerospace target identification

Abstract

Two identification approaches, the matching score (MS) method and the neural network (NN) method, and multiple polarization data are utilized to identify aerospace targets. A majority vote rule, a maximum isolation distance rule, and a combination of these two rules are proposed to determine a target class when four polarization combinations data are available. It is found that by combining the two decision rules, the recognition rates can be greatly improved. The effect of Gaussian noise on the recognition rates with the MS method and the NN method is also studied. If only the factor of Gaussian noise is considered, it is found that the MS method is more robust to Gaussian noise contamination than the NN method when the network is trained only by the uncontaminated range profiles.