Optimization of Subarray Partition for Large Planar Phased Array Radar Based on Weighted K-Means Clustering Method

Xiaopeng Yang, Wen Xi, Yuze Sun, Tao Zeng, Teng Long, Tapan K. Sarkar

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


Subarray partition is indispensable in large phased array radar system for reducing the manufacturing cost as well as realizing the system potentiality. The optimization of subarray partition for large planar phased array radar according to weighted K-means clustering method is mainly investigated in this paper. Based on the excitation matching technique, the optimization of subarray partition in monopulse application can be reformulated as a clustering of reference gain ratios to minimize the excitation matching error. However, when the element weights are non-uniform for specific intentions such as low sidelobes, the matching error could not be minimized completely by traditional K-means clustering. Therefore, in this paper, a weighted K-means clustering method is proposed to reduce the matching error by modifying the membership rule and cluster center of K-means clustering. The proposed method can provide smaller matching error compared with conventional clustering methods, especially when the elements are weighted non-uniformly. The effectiveness of proposed method is validated by numerical simulations and compared with several classical clustering methods.

Original languageEnglish (US)
Article number7181641
Pages (from-to)1460-1468
Number of pages9
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number8
StatePublished - Dec 2015


  • Excitation matching
  • K-means clustering method
  • pattern matching
  • subarray partition

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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