Robust UWB radar target classification in white Gaussian noise based on Matrix Pencil Method in Frequency Domain and Mahalanobis Distance

In this paper, we propose a robust method for UWB automatic radar classification in white Gaussian noise and different aspect angles between the radar and the target. The method is based on the use of Matrix Pencil Method in Frequency Domain (MPMFD) for feature extraction and Mahalanobis Distance for classification. In order to test the accuracy of the proposed method, we have used complex target geometries modeled by perfectly conducting, straight, thin wires. Simulation results show that accurate results of radar target classification can be obtained by the proposed method. In addition, we prove that the proposed method has better ability to tolerate noise effects in radar target classification.