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Training by ART-2 and Classification of Ballistic Missiles Using Hidden Markov Model

  • Upendra Kumar Singh
  • Vineet Padmanabhan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8251)

Abstract

This paper addresses the classification of different ranges of Ballistic Missiles (BM) for air defense applications using Adaptive Resonance Theory (ART-2) and Hidden Markov Model (HMM). ART-2 finds the initial clusters using unsupervised learning to be fed to HMM for classification using recursive method. The classification is based on derived parameters of specific energy, acceleration, altitude and velocity which in turn are acquired from measured data by radars. To meet the conflicting requirements of classifying short as well as long-range BM trajectories, we are proposing a formulation for partitioning the trajectory by using a moving window concept. Experimental results show that the HMM model is able to classify above 95% within time of the order of milliseconds once initial data is trained using ART2.

Keywords

Hide Markov Model Adaptive Resonance Theory Hide Markov Model Model Ballistic Missile M2000 Class 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Upendra Kumar Singh
    • 1
  • Vineet Padmanabhan
    • 2
  1. 1.PGAD, Defence Research & Development OrganisationHyderabadIndia
  2. 2.School of Computer & Information SciencesUniversity of HyderabadIndia

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