小波域的最大似然调制分类

Maximum likelihood classification for MPSK signals in the wavelet domain

针对电子战领域中的调制类型识别问题,提出了一种把通信信号变换到小波域下的最大似然调制分类算法.通过分析MPSK信号在Haar小波基下的小波变换系数同其相位参数之间的关系,把MPSK信号的小波变换系数用广义高斯概率分布进行建模来得到MPSK信号的最大似然分类函数.同常规的最大似然分类算法相比新方法所需的先验信息少.计算机仿真结果表明,这种算法在低信噪比下仍然能获得好的正确分类性能.

With the problem of identifying modulation types in electronic warfare in mind, a novel maximum likelihood （ML） modulation classification algorithm is presented in the wavelet transform domain. The relationship between the Haar wavelet transform coefficients of the MPSK signals and its phase parameters is discussed, with the ML classification function obtained by modeling the wavelet coefficients as a generalized Gaussian distribution. Less priori parameters knowledge is needed in the new algorithm. Numerical experiments are also used to illustrate the effectiveness and robustness of the proposed method.