Robust Target Identification in White Gaussian Noise Using Canonical Correlation Analysis

In order to correctly identify a remote target, a robust target signatures identification technique is required. Radar target identification based on complex natural resonances (CNRs) has drawn the interest of many researchers following the development of the singularity expansion method (SEM). CNRs are popular due to the fact that they are theoretically independent of the aspect angle between the radar and the target, and they form a minimal set of parameters by which the target can be identified thus assisting the classification problem. As evident from the literature, statistical techniques such as the generalized likelihood ratio test (GLRT) have produced a better identification result, in the presence of noise, compared to some other SEM based identification methods such as the extinction pulse (E-pulse) technique. In this communication, we develop yet another novel statistical method based on canonical correlation analysis (CCA) to perform target classification. Simulation results using various targets show that our method is comparable to the GLRT method in the presence of white Gaussian noise.