Radar Target Recognition using Time-Frequency Analysis and Polar Transformation

A new method for Automatic Radar Targets Recognition is presented based on Inverse Synthetic Aperture Radar (ISAR). In this work, the first step is to construct ISAR images via a Non uniformly Sampled Bivariate Empirical Mode Decomposition Time-Frequency Distribution (NSBEMD-TED) method. Indeed, this Time-Frequency representation is well suited for non-stationary signals analysis and provides high resolution with good accuracy. The obtained ISAR images is used to provide the evolution of two-dimensional spatial distribution of a moving target and, therefore, its are suitable to be used for radar target recognition tasks. In second step, a feature vectors are extracted from each ISAR images in order to describe the discriminative informations about a target. In the features extraction step, we computed several rings of polar space applied on ISAR image. Then, these rings is projected on 1-D vector. To ensure translation invariance of the obtained projected 1-D vector, a Fourier Descriptors are computed. In third step of this work, the recognition task is achieved using k-Nearest Neighbors (K-NN), Fuzzy k-NN, Neural network and Bayesian classifiers. To validate our approach, simulation results are presented on a set of several targets constituted by ideal point scatterers models.