摘要
针对现有雷达脉内特征参量对噪音敏感,难以适应复杂体制雷达聚类分选的问题,应用双谱多类小波包特征实现了宽信噪比条件下未知复杂雷达信号的高准确率聚类分选.首先对接收到的雷达信号求得双谱归一化系数,然后利用多类小波包提取双谱归一化系数的特征参量,并选取最佳小波包特征作为分选参量,最后通过提取8类雷达辐射源信号的双谱小波包特征,采用核模糊C均值聚类算法实现聚类分选.仿真结果表明:提取的特征参量在宽信噪比范围内均具有很好的分离性和稳定性,可实现复杂雷达辐射源信号的准确聚类分选.
Radar common intra-pulse characteristics are sensitive to signal-noise ratio, thus they are not adaptable to the complex radar systems. To solve this problem, a method was proposed which applied the multi-wavelet packets characteristics of bispectrum to sort unknown complicated radar signals under the large-scale signal-noise ratio condition with a high sorting rate. The bispectrum of received signals was extracted and predigested to two dimensions characteristic. Then, multi-wavelet packets were used to extract characteristics from two dimensions of the bispectrum and the best characteristics were selected as the sorting parameters. The best characteristics of eight classes radar emitter signals were extracted, then kernelized fuzzy c-means was used to cluster and sort signals. The experiment results demonstrate that the characteristics of eight typical radar emitter signals extracted by this method have good performance on noise-resistance and clustering with the large-scale signal-noise ratio.
出处
《光子学报》
EI
CAS
CSCD
北大核心
2014年第3期152-159,共8页
Acta Photonica Sinica
基金
国家科技支撑计划重点项目(No.2011BAH24B05)资助
关键词
雷达辐射源
聚类分选
双谱
小波包
核模糊C均值
Radar emitter signals
Cluster and sort
Bispectrum
Wavelet packet
Kernelized fuzzy c- means
