摘要
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 dis- tribution 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 re- sults 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.
出处
《中山大学学报(自然科学版)》
CAS
CSCD
北大核心
2014年第1期42-46,共5页
Acta Scientiarum Naturalium Universitatis Sunyatseni
基金
国家自然科学基金资助项目(61072070
61301179)
教育部博士学科点基金资助项目(20110203110011)
ISN国家重点实验室自主课题资助项目(ISN1101002)
广东省科技计划资助项目(2012B010100038)
广东省-教育部产学研结合资助项目(2012B091100364)
惠州市科技计划资助项目(2011C020005005)
关键词
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 algorithm
mixed modulation signal
envelope variance of mean normalization
modulation recognition