导数约束平滑条件下基于模糊函数特征的雷达辐射源信号识别方法

针对低信噪比条件下，雷达辐射源信号识别效果差的问题，提出了一种导数约束平滑条件下提取信号模糊函数特征的辐射源信号识别方法.建立了基于取整函数和坐标转换的模糊函数最大能量角提取的数学模型，降低处理复杂度；提出了不依赖于信号及噪声具体模型的基于导数约束平滑的最大能量切片波形信息提取算法，转化为二阶锥规划（Second-order Cone Programming，SOCP）问题求解，较大程度地降低了噪声对模糊函数波形特征的影响；依据有效性指标，确定了本文算法中目标函数正则化系数与对称Holder系数的范数因子取值，最后通过模糊c-means方法实现对辐射源信号特征向量的聚类识别.仿真结果表明，在低信噪比条件下本文方法具有更高的识别正确率.

Radar Emitter Recognition Based on Ambiguity Function Features with Derivative Constraint on Smoothing

To improve the performance of radar emitter recognition under low signal-to-noise ratio,a method that extracts features from ambiguity function with derivative constraint on smoothing is proposed.A mathematical model to obtain the max energy angle based on rounding function and coordinate transformation is set,greatly reducing processing complexity.An algorithm picking the waveform of max energy slice is also presented referring to derivative constraint on smoothing.It depends on none specific signal and noise models.The algorithm is transferred into Second-order Cone Programming (SOCP) for solving and it weakens the influence caused by noise upon ambiguity function waveform features to a great extent.According to the validity index,values of the regularization parameter in objective function and the norm parameter in symmetrical Holder coefficient are determined.Finally,fuzzy c-means clustering is implemented for classification and recognition of feature vectors extracted from emitter signals.Simulation results indicate that the method presented in the paper gains higher correct recognition rate.