SVM and greedy GMM applied on target identification

  • Authors:

  • Dalila Yessad;Abderrahmane Amrouche;Mohamed Debyeche

  • Affiliations:

  • Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Bab Ezzouar, Algiers, Algeria;Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Bab Ezzouar, Algiers, Algeria;Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Bab Ezzouar, Algiers, Algeria

  • Venue:
  • ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
  • Year:
  • 2011

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Abstract

This paper is focused on the Automatic Target Recognition (ATR) using Support Vector Machines (SVM) combined with automatic speech recognition (ASR) techniques. The problem of performing recognition can be broken into three stages: data acquisition, feature extraction and classification. In this work, extracted features from micro-Doppler echoes signal, using MFCC, LPCC and LPC, are used to estimate models for target classification. In classification stage, three parametric models based on SVM, Gaussian Mixture Model (GMM) and Greedy GMM were successively investigated for echo target modeling. Maximum a posteriori (MAP) and Majority-voting post-processing (MV) decision schemes are applied. Thus, ASR techniques based on SVM, GMM and GMM Greedy classifiers have been successfully used to distinguish different classes of targets echoes (humans, truck, vehicle and clutter) recorded by a low-resolution ground surveillance Doppler radar. The obtained performances show a high rate correct classification on the testing set.