Performance Analysis of Hybrid Pre-Processing Techniques of Ground Target Classification in FSR using Neural Network

Abstract

This paper presents an enhanced pattern recognition neural network classifier for ground target classification in FSR. The hybrid enhanced model is based on a different combination of pre-processing techniques. Wavelet and feature extraction techniques were applied on time-domain raw data of three vehicles before it is processed by the neural network classifier. Several scenarios were undertaken to study and analyze the effect of adding the proposed techniques to the NN classifier’s performance. The result reveals the potential and effectiveness of pre-processing to increase the performance by 79.2% to previous work, which gives more than 97% true classification.