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数字调制识别
相关语句
  digital modulation recognition
     Digital modulation recognition based on ART2A-E neural network
     基于ART2A-E神经网络的数字调制识别
短句来源
     A new method of automatic digital modulation recognition based on ART2A-E neural network has been proposed after analyzing the traditional structure of ART2/2A neural network, and the ability of the proposed classifier are evaluated with six different modulation types such as 2PSK,4PSK,OQPSK,GMSK,π/4OQPSK and 4ASK.
     在分析经典的ART2/2A网络基础上,提出了一种基于ART2A-E神经网络的自动数字调制识别分类器,并测试了分类器对2PSK、4PSK、OQPSK、GMSK、π/4OQPSK、4ASK6种数字调制信号的识别能力.
短句来源
     Automatic Digital Modulation Recognition Using Moving Average and Higher Statistics
     基于滑动平均和高阶统计量的数字调制识别
短句来源
     A NEW DIGITAL MODULATION RECOGNITION METHOD USING FEATURES EXTRACTED FROM GAR MODEL PARAMETERS
     从GAR模型参数提取特征的数字调制识别新方法
短句来源
     A Digital Modulation Recognition Algorithm Based on Support Vector Machines
     一种基于支持向量机的数字调制识别方法
短句来源
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  digital modulation identification
     A Study on the Digital Modulation Identification Technologies Based on Wavelet Transform
     基于小波变换的数字调制识别技术研究
短句来源
  “数字调制识别”译为未确定词的双语例句
     Application of Digital Automatic Modulation Recognition in Telemetry Simulate System
     遥测信号仿真系统中的数字调制识别应用
短句来源
     Based on the algorithms of Nandi and Azzouz,the paper developed an automatic recognition algorithms for MASK,MFSK and MPSK.
     在Nandi和Azzouz提出的数字调制识别算法基础上,针对MASK、MFSK和MPSK 3大类多进制数字调制信号,提出了一种基于瞬时参量峰度的自动识别算法。
短句来源
     Research and Application on an Effective Combination Classifier of Neural Network
     基于快速组合神经网络分类器的数字调制识别新方法
短句来源
     A Method of Modulation Classification Based on HOC and a Covering Algorithm for Constructing Neural Networks
     基于高阶累量与神经网络覆盖算法的数字调制识别
短句来源
     Study on Recognition of M-ary Digital Modulation Signals
     多进制数字调制识别算法研究
短句来源
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  digital modulation recognition
A new digital modulation recognition method using features extracted from GAR model parameters
      


A new digital modulation recognition method is proposed, which is based on the features extracted from generalized autoregressive (GAR) model parameters of the obsevered sequence and the use of neural network classifier. Because of the better noise suppression capacity of the GAR model and the powerful pattern classification capacity of the neural network classifier, the new method can significantly improve the recognition performance in lower SNR. Computer simulations also show that the new method possesses...

A new digital modulation recognition method is proposed, which is based on the features extracted from generalized autoregressive (GAR) model parameters of the obsevered sequence and the use of neural network classifier. Because of the better noise suppression capacity of the GAR model and the powerful pattern classification capacity of the neural network classifier, the new method can significantly improve the recognition performance in lower SNR. Computer simulations also show that the new method possesses higher recognition ratio and better robustness.

本文提出了一种从观察序列的广义自回归(GAR)模型参数提取待识别信号的伪瞬时中心频率和伪瞬时3dB带宽特征,并利用神经网络分类器的数字调制识别新方法。这种方法充分利用了GAR模型良好的抗噪声能力和神经网络优异的模式分类能力,能有效地改善低SNR条件下的调制识别性能。计算机模拟结果证实了该方法具有很高的识别率和良好的稳健性。

A new method of automatic digital modulation recognition based on ART2A-E neural network has been proposed after analyzing the traditional structure of ART2/2A neural network, and the ability of the proposed classifier are evaluated with six different modulation types such as 2PSK,4PSK,OQPSK,GMSK,π/4OQPSK and 4ASK. Simulation results showed that the success rates for the six kinds of digitally modulated signals were all higher than 90% at 8 dB Signal to Noise Ratio (SNR), and a new pattern could be clustered...

A new method of automatic digital modulation recognition based on ART2A-E neural network has been proposed after analyzing the traditional structure of ART2/2A neural network, and the ability of the proposed classifier are evaluated with six different modulation types such as 2PSK,4PSK,OQPSK,GMSK,π/4OQPSK and 4ASK. Simulation results showed that the success rates for the six kinds of digitally modulated signals were all higher than 90% at 8 dB Signal to Noise Ratio (SNR), and a new pattern could be clustered into new domains automatically without overlaying the original memory patterns, the property of expansibility and self-adaptation is improved and the error rate is decreased.

在分析经典的ART2/2A网络基础上,提出了一种基于ART2A-E神经网络的自动数字调制识别分类器,并测试了分类器对2PSK、4PSK、OQPSK、GMSK、π/4OQPSK、4ASK6种数字调制信号的识别能力.仿真结果表明,该分类器对这6种数字调制信号在信噪比为8dB条件下识别率达到90%以上,而且在不影响网络已记忆模式情况下能自动对未学习模式进行新的聚类,提高了自动调制分类器的自适应性和可扩展性,减少了错判概率.

Automatic modulation recognition (AMR) is the basis of controlling demodulation unit in multi- mode software radio re- ceivers, therefore more and more researcher' s attention is put on it now. In this paper, a new AMR algorithm based on sup- port vector machines (SVM) is presented. Compared with tradi- tional algorithms based on neural networks, this algorithm has better generalization ability. The performance of the algorithms has been evaluated by simulating different types of band- limited digital signals...

Automatic modulation recognition (AMR) is the basis of controlling demodulation unit in multi- mode software radio re- ceivers, therefore more and more researcher' s attention is put on it now. In this paper, a new AMR algorithm based on sup- port vector machines (SVM) is presented. Compared with tradi- tional algorithms based on neural networks, this algorithm has better generalization ability. The performance of the algorithms has been evaluated by simulating different types of band- limited digital signals corrupted by white Gaussian noise. It is found that the overall success rate is over 97% at SNR = 15 dB.

信号调制方式的自动识别是控制多模式软件无线电接收机解调单元对信号进行正确解调的基础,因此近年来得到了越来越多的研究者的关注。本文提出了一种基于支持向量机的数字调制识别方法。与传统的神经网络方法相比该方法具有更好的泛化推广能力。计算机仿真表明,当噪声采用高斯白噪声,并且信噪比大于15dB时,正确识别率高于97%。

 
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