Laser & Optoelectronics Progress, Volume. 56, Issue 6, 062804(2019)
Applications of Partial Connection Clustering Algorithm and Random Forest Algorithm in Radar Signal Sorting
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.
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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
Category: Remote Sensing and Sensors
Received: Sep. 19, 2018
Accepted: Oct. 17, 2018
Published Online: Jul. 30, 2019
The Author Email: Zhang Mengmeng (mengzhangm@qq.com), Liu Yi'an (lya_wx@jiangnan.edu.cn)