激光与光电子学进展, 2019, 56 (6): 062804, 网络出版: 2019-07-30
偏联系数聚类和随机森林算法在雷达信号分选中的应用 下载: 941次
Applications of Partial Connection Clustering Algorithm and Random Forest Algorithm in Radar Signal Sorting
遥感 信号分选 偏联系聚类算法 教与学随机森林算法 集对分析 remote sensing signal sorting partial connection clustering algorithm teaching and learning random forest algorithm set pair analysis
摘要
为了提高雷达调制信号在电子对抗环境中的分选准确度,建立了基于偏联系数模糊聚类(PCFCM)算法和教与学随机森林(TLRF)算法的雷达调制信号分选(PCFCM-TLRF)模型。该模型引入偏联系数(PCN)改进K均值聚类(K-means)算法,优化模糊C均值聚类(FCM)算法,用优化后的FCM算法对信号样本集进行预处理;使用“教与学”优化(TLBO)算法优化随机森林(RF)算法,使优化后的RF算法能够以更低的复杂度构成更优的分类器;将预处理后的样本作为TLRF中的训练样本实现信号分选。研究结果表明,与其他分选模型相比,PCFCM-TLRF模型具有更高的分选准确度,能够有效地实现雷达调制信号的分选。
Abstract
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.
张萌萌, 刘以安, 宋萍. 偏联系数聚类和随机森林算法在雷达信号分选中的应用[J]. 激光与光电子学进展, 2019, 56(6): 062804. 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.