RADAR TARGET RECOGNITION AND CLASSIFICATION USING SUPERVISED MACHINE LEARNING APPRAOCHES

Abstract

Target classification from the returned echo signals is one of the challenging problems in the modern RADAR systems. The key feature that is used for the target classification is the Radar Cross Section (RCS).  The recent advancements in the field of machine learning techniques gave interesting results for the RADAR target recognition. A dedicated machine learning models are realized to recognize simple and complex targets. The models corresponding to both simple and complex target recognition are developed with a capability to identify four common geometrical structures namely circular cylinder, frustum (truncated cone), circular disc and sphere. The proposed method extracts features of simple targets by using Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). The unknown targets are classified with the feature extraction set obtained using different supervised classifiers namely k- Nearest Neighbor (k-NN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and their performance is compared. The k-NN classifier gives better performance of classification accuracy when compared to existing methods.