• Laser & Optoelectronics Progress
  • Vol. 56, Issue 6, 062804 (2019)
Mengmeng Zhang**, Yi'an Liu*, and Ping Song
Author Affiliations
  • School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • show less
    DOI: 10.3788/LOP56.062804 Cite this Article
    Mengmeng Zhang, Yi'an Liu, Ping Song. Applications of Partial Connection Clustering Algorithm and Random Forest Algorithm in Radar Signal Sorting[J]. Laser & Optoelectronics Progress, 2019, 56(6): 062804 Copy Citation Text show less


    In order to improve the sorting accuracy of radar modulated signals in the electronic countermeasure environment, based on the partial connection fuzzy C-means (PCFCM) algorithm and the teaching-learning random forest (TLRF) algorithm, a radar modulated signal sorting model PCFCM-TLRF is proposed. In this model, we introduce the partial connection number (PCN) to improve the K-means clustering algorithm and optimize the fuzzy C-means (FCM) algorithm. Then the signal sample is pre-processed with the improved FCM algorithm. The teaching-learning-based optimization (TLBO) algorithm is used to optimize the random forest (RF) algorithm, so that the optimized RF algorithm can form a better classifier with much lower complexity. The pre-processed sample is used as the training sample in the TLRF algorithm to realize the sorting of radar signals. The research results show that the sorting accuracy of the PCFCM-TLRF model is higher than those of other sorting models. This model can realize the effective sorting of radar modulated signals.