Classification of correlation signatures of spread spectrum signals using neural networks

Proceedings of the IEEE 1991 National Aerospace and Electronics Conference NAECON 1991 Pub Date : 1991-05-20 DOI:10.1109/NAECON.1991.165794

R. A. Chapman, D. Norman, D. Zahirniak, S. Rogers, M. Oxley

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引用次数: 4

Abstract

The authors discuss the application of artificial neural networks (ANNs) to the classification of spread spectrum signals based on signal type or spreading technique. Radial basis function networks (RBFNs) and back-propagation networks (BPNs) were used to classify the correlation signatures of the signals. Correlation signatures of four types or classes were obtained from United States Army Harry Diamond Laboratories: direct sequence (DS), linearly stepped frequency hopped (LSFH), randomly driven frequency hopped (RDFH), and a hybrid of direct sequence and randomly driven frequency hopped (HYB). RBFNs and BPNs trained directly on two classes (DS and LSFH) and four classes (DS, LSFH, RDFH, and HYB) of correlation signatures. Classification accuracies ranged from 79% to 92% for the two-class problem and from 70% to 76% for the four-class problem. The RBFNs consistently produced classification accuracies from 5% to 10% higher than accuracies produced by the BPNs. The RBFNs produced this classification advantage in significantly less training for all cases.<>

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基于神经网络的扩频信号相关特征分类

讨论了基于信号类型或扩频技术的人工神经网络在扩频信号分类中的应用。采用径向基函数网络(RBFNs)和反向传播网络(BPNs)对信号的相关特征进行分类。从美国陆军哈里钻石实验室获得了直接序列(DS)、线性阶跃跳频(LSFH)、随机驱动跳频(RDFH)和直接序列与随机驱动跳频(HYB)混合的4种类型或类别的相关特征。rbfn和bpn直接在两类(DS和LSFH)和四类(DS、LSFH、RDFH和HYB)相关特征上进行训练。两类问题的分类准确率为79%到92%，四类问题的分类准确率为70%到76%。rbfn产生的分类准确率始终比bpn产生的准确率高5%至10%。在所有情况下，rbfn在显著减少训练的情况下产生了这种分类优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the IEEE 1991 National Aerospace and Electronics Conference NAECON 1991

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