Online clustering algorithms for radar emitter classification

Jun Liu, Jim P.Y. Lee, Lingjie Li, Zhi Quan Luo, K. Max Wong

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Radar emitter classification is a special application of data clustering for classifying unknown radar emitters from received radar pulse samples. The main challenges of this task are the high dimensionality of radar pulse samples, small sample group size, and closely located radar pulse clusters. In this paper, two new online clustering algorithms are developed for radar emitter classification: One is model-based using the Minimum Description Length (MDL) criterion and the other is based on competitive learning. Computational complexity is analyzed for each algorithm and then compared. Simulation results show the superior performance of the model-based algorithm over competitive learning in terms of better classification accuracy, flexibility, and stability.

Original languageEnglish (US)
Pages (from-to)1185-1196
Number of pages12
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume27
Issue number8
DOIs
StatePublished - Aug 1 2005

Keywords

  • Cluster validation
  • Clustering
  • Competitive learning
  • Computational complexity
  • Emitter classification
  • MDL criterion
  • Online process

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