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偏联系数聚类和随机森林算法在雷达信号分选中的应用

Applications of Partial Connection Clustering Algorithm and Random Forest Algorithm in Radar Signal Sorting

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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.

Newport宣传-MKS新实验室计划
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中图分类号:TN958

DOI:10.3788/lop56.062804

所属栏目:遥感与传感器

基金项目:国家自然科学基金(21706096)

收稿日期:2018-09-19

修改稿日期:2018-10-14

网络出版日期:2018-10-17

作者单位    点击查看

张萌萌:江南大学物联网工程学院,江苏 无锡 214122
刘以安:江南大学物联网工程学院,江苏 无锡 214122
宋萍:江南大学物联网工程学院,江苏 无锡 214122

联系人作者:刘以安(lya_wx@jiangnan.edu.cn); 张萌萌(mengzhangm@qq.com);

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引用该论文

Zhang Mengmeng,Liu Yi′an,Song Ping. Applications of Partial Connection Clustering Algorithm and Random Forest Algorithm in Radar Signal Sorting[J]. Laser & Optoelectronics Progress, 2019, 56(6): 062804

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

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