利用MATLAB，基于高阶累积量对调制系统的识别进行了仿真，包括2ASk，2PSK，2FSK，4ASk，4PSK，4FSK等调制方式，并且对识别率以及判决的门限进行仿真

高阶累积量对ASK PSK FSK调制方式的识别，MATLAB运行代码，MATLAB2016b运行无误

高阶累积量HOSA工具箱 hosa hosa\ARMAQS.M hosa\ARMARTS.M hosa\ARMASYN.M hosa\ARORDER.M hosa\ARRCEST.M hosa\BICEPS.M hosa\BICEPSF.M hosa\BICOHER.M hosa\BICOHERX.M hosa\BISPECD.M hosa\BISPECDX.M hosa\BISPECI.M hosa\BISPECT.M hosa\CONTENTS.M hosa\CUM2EST.M hosa\CUM2X.M hosa\CUM3EST.M hosa\CUM3X.M hosa\CUM4EST.M hosa\CUM4X.M hosa\CUMEST.M hosa\CUMTRUE.M hosa\DOA.M hosa\DOAGEN.M hosa\GLSTAT.M hosa\HARMEST.M hosa\HARMGEN.M hosa\HOSAHELP.M hosa\HOSAVER.M hosa\HPRONY.M hosa\INFO.XML hosa\IVCAL.M hosa\MAEST.M hosa\MAORDER.M hosa\MATUL.M hosa\NLGEN.M hosa\NLPOW.M hosa\NLTICK.M hosa\PICKPEAK.M hosa\QPCGEN.M hosa\QPCTOR.M hosa\README.M hosa\RIVDL.M hosa\RIVTR.M hosa\RPIID.M hosa\TDE.M hosa\TDEB.M hosa\TDEGEN.M hosa\TDER.M hosa\TLS.M hosa\TRENCH.M hosa\TRISPECT.M hosa\WIG2.M hosa\WIG2C.M hosa\WIG3.M hosa\WIG3C.M hosa\WIG4.M hosa\WIG4C.M

FSK的类内识别，采用两种方法进行分类 https://blog.csdn.net/wlwdecs_dn/article/details/114462996

对于传统的基于高阶累积量调制信号识别已经较为成熟，从基本的单混合信号出发，提取合适的特征参数设计相应的分类器，实现单混合信号的识别，现在达到了一个较高的识别率。具有一定的工程意义，本资源主要包括一些基本的高阶累积量代码实现，和一些基于高阶累积量来实现的混合信号识别的文献，对于入门学习高阶累积量和信号识别具有一定的指导意义。

There is much more information in a stochastic non-Gaussian or deterministic signal than is conveyed by its autocorrelation and power spectrum. Higher-order spectra which are defined in terms of the higher-order moments or cumulants of a signal, contain this additional information. The Higher-Order Spectral Analysis (HOSA) Toolbox provides comprehensive higher-order spectral analysis capabilities for signal processing applications. The toolbox is an excellent resource for the advanced researcher and the practicing engineer, as well as the novice student who wants to learn about concepts and algorithms in statistical signal processing. The HOSA Toolbox is a collection of M-files that implement a variety of advanced signal processing algorithms for the estimation of cross- and auto-cumulants (including correlations), spectra and olyspectra,bispectrum, and bicoherence, and omputation of time-frequency distributions. Based on these, algorithms for parametric and non-parametric blind system identification, time-delay estimation, harmonic retrieval, phase-coupling, direction of arrival estimation, parameter estimation of Volterra (non-linear) models, and adaptive linear prediction are implemented. Also included are algorithms for testing of Gaussianity and Linearity of a time series. A full tutorial and demo set are included in the toolbox.

大牛开发的高阶循环累积量工具包，很有参考价值，可以推导更高阶的循环累积量

计算信号的高阶累积量，带注释，包括高阶矩，亲测可用，代码是matlab的。

通信技术的快速发展使得数字通信信号的调制类别变得愈加多样化，通信信号调制识别 不仅在民用方面发挥着巨大的作用而且军用方面同样扮演着重要的角色。调制识别在信号处 理领域已成为热点研究课题，

比较四阶累积量和二阶矩的DOA性能，在高斯色噪声和白噪声条件下比较两者的性能