1. 佛山科学技术学院 电子与信息工程学院,广东,佛山,528000
2.
3. 西安电子科技大学 综合业务网理论及关键技术国家重点实验室,陕西,西安,710071
纸质出版日期:2014,
网络出版日期:2014-1-25,
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杨发权, 李赞, 罗中良. 混合调制信号调制识别方法[J]. 中山大学学报(自然科学版)(中英文), 2014,53(1):42-46.
YANG Faquan, LI Zan, LUO Zhongliang. Method of Modulation Recognition of Mixed Modulation Signal[J]. Acta Scientiarum Naturalium Universitatis SunYatseni, 2014,53(1):42-46.
研究基于决策理论算法的混合调制信号特征参数提取与自动识别技术,提出适合混合调制信号调制识别的树型分类器及相应识别步骤。在外调制、内调制识别时首次分别采用副载波信号个数构成的特征矢量、均值归一化包络方差、副载波信号瞬时幅度分布区域统计值等算法,抑制噪声干扰,提高特征参数的准确性,仿真结果表明,在信噪比为6 dB情况下,调制识别率接近90%,和现有混合调制识别方法相比取得较好的识别效果,在混合信号调制识别管理中具有广泛的应用前景。
Based on decision theory algorithm,the characteristic parameter extraction and automatic identification technology of mixed modulation signal are researched
and then the tree classifier with identification steps which are suitable for mixed modulation signal modulation recognition are put forward. The characteristic vector which are composition of the number of subcarrier signal
envelope variance of mean normalization and algorithm of the statistical value of subcarrier signal instantaneous amplitude distribution area are first used in recognition of outer modulation and inner modulation respectively so as to reduce the noise interference and improve the accuracy of characteristic parameters. The simulation results show that modulation recognition rate is close to 90% under the condition of SNR which is 6 dB and it has a good recognition effect compared with mixed modulation recognition method existing and a broad prospect of application in the management of the mixed signal modulation identification.
树型分类器算法混合调制信号均值归一化包络方差调制识别
tree classifier algorithmmixed modulation signalenvelope variance of mean normalizationmodulation recognition
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