电台识别的云模型算法研究

时频域的电台识别较多利用频率稳定度作为特征，但在采集环境和设备噪声的限制下，频率估计精度难以提高，影响电台的识别率。本文在分析瞬时频率的概率分布函数的基础上，结合云模型分类器，提出了改进相位拟合的方法。首先将经过预处理的数据进行相位拟合，得到短时频率估计值，然后利用估计值建立正态云，最后转化为隶属度进行分类识别。这种方法淡化了非彼即此的硬判分类，弥补了分类过程中对特征的模糊性和随机性的忽视。仿真实验和实际电台实验验证了在算法运算代价基本相同的情况下，识别率有了较大的提高，最高的平均识别率达到了97%。 

Research on Algorithms of Cloud Model Used in Radio Identification

The frequency stability of such signals in time-frequency domain was widely used to be as a feature to identify specific emitters, such as civil or military radio. But the acquisition environment and equipment noise restricted the improvement of the recognition rate even though the frequency estimation accuracy is high enough. Based on the analysis of the instantaneous frequency of the probability distribution function, this paper, combined with the cloud model method of classification and recognition, presented the improved phase fitting algorithm. In the first place of this paper, the data which has been preprocessed was used in the phase fitting procedure; the second step was to obtain short time frequency estimates, which was used to establish the normal cloud model; at the end of experiment, the preprocessed data was applied for classification. This method has improved the ambiguity of the threshold judgment and improved the embodiment of fuzziness and randomness. The experimental results of the simulation and practical signals show that the identification of the suggested technique can reach 97%, which is more accurate than conventional methods, even when at the expense of not using excessive additional time.