Micro-Doppler classification for ground surveillance radar using speech recognition tools

  • Authors:

  • Dalila Yessad;Abderrahmane Amrouche;Mohamed Debyeche;Mustapha Djeddou

  • Affiliations:

  • Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Algiers, Algeria;Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Algiers, Algeria;Speech Communication and Signal Processing Laboratory, Faculty of Electronics and Computer Sciences, USTHB, Algiers, Algeria;Communication Systems Laboratory, Ecole Militaire Polytechnique, BEB, Algiers, Algeria

  • Venue:
  • CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

Among the applications of a radar system, target classification for ground surveillance is one of the most widely used. This paper deals with micro-Doppler Signature (μ-DS) based radar Automatic Target Recognition (ATR). The main goal for performing μ-DS classification using speech processing tools was to investigate whether automatic speech recognition (ASR) techniques are suitable methods for radar ATR. In this work, extracted features from micro-Doppler echoes signal, using MFCC, LPC and LPCC, are used to estimate models for target classification. In classification stage, two parametric models based on 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 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. Experimental results show that MV post processing improves target recognition and the performances reach to 99,08% correct classification on the testing set.