利用循环相关特征的自适应模糊调制分类

针对非平稳数字调制信号，提出一种高阶正交循环累量特征，具有“屏蔽”干扰和噪声的特性. 采用模糊神经网络非线性动态建模的调制识别器，根据特征训练样本的大致分布状况建立蕴涵初始经验的模糊推理系统结构，再嵌入神经网络的结构和自适应学习算法对模糊系统参数进行调整和优化，完成模糊神经网络模型的逼近求精. 对MASK, MPSK, MFSK, MQAM 等信号进行仿真实验，结果表明系统在信噪比等环境参数变化较大的情况下适应性和容错性良好. 相对于神经网络等识别器，具有初始经验的系统结构更明确，建模周期较短，算法识别率和效率有明显提高.

Adaptive Fuzzy Classification of Modulation Using Cyclic Correlation Feature

For non-stationary digitally modulated signals, a high definition feature, high order cyclic cross cumulant(HOCCC) for is proposed to suppress interference and noise. A novel modulation classifier based on nonlinear dynamic fuzzy neural networks (FNN) is presented. According to the general distribution of the feature samples, we establish a fuzzy inference system with initial experiences, embed the structure and self-adaptive training of the neural network to adjust and optimize the fuzzy system parameter, and complete approximation of the fuzzy neural network modeling. For MASK, MPSK, MFSK and MQAM, simulation results show betteradaptability and fault-tolerance of the system at a variety of environment parameters such as SNR. The system with initial experiences possesses a short modeling phase, and can improve average probability of correct classification and efficiency compared to neural network classifiers.