Systems Engineering and Electronics ›› 2022, Vol. 44 ›› Issue (9): 2743-2751.doi: 10.12305/j.issn.1001-506X.2022.09.06

• Electronic Technology • Previous Articles     Next Articles

An improved GMM clustering based on data field and decision graph

Lei WANG1,*, Zhiyong ZHANG1, Weigui ZENG1, Silei CAO1, Tianhe ZHANG2   

  1. 1. Coastal Defense College, Naval Aviation University, Yantai 264001, China
    2. Unit 91827 of the PLA, Weihai 264000, China
  • Received:2021-11-18 Online:2022-09-01 Published:2022-09-01
  • Contact: Lei WANG

Abstract:

Aiming at the problems of traditional clustering methods in processing radar signals in complex electromagnetic environment, such as low clustering quality, manual parameter setting and poor tolerance to isolated noise pulses, an improved Gaussian mixture model (GMM) clustering algorithm based on data field and decision graph is proposed. The data field theory is applied to the representation of the density of data objects, the potential energy distance decision graph is generated, and then the selection of cluster number and center point is realized automatically. Finally, the clustering division of data objects is realized combined with GMM. The simulation results show that this method has better sorting effect than the existing typical classification methods when there are significant jitter and measurement error in direction of arrival, pulse width and radio frequency, with the isolated noise pulses interference and pulse loss in the meantime.

Key words: radar signal classification, data field, decision diagram, Gaussian mixture model (GMM) clustering

CLC Number: 

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