With the rapid development of electronic technology and its widespread application in modern warfare, the modulation methods of individual radar signal sources are more flexible, the parameters are diverse, and the modern battlefield electromagnetic signal environment is complex, so that radar signal recognition based on traditional conventional parameters Technology cannot meet real needs. Among the many radar signal modulation recognition algorithms that have appeared in recent years, neural network theory and intra-pulse analysis algorithms are widely used, but they each have their own shortcomings, and a single method cannot meet the needs of increasingly complex modulation signal recognition and parameter estimation needs. . Based on the above background, the research content of this paper is to identify the subtle characteristics of radar signals and their parameter estimation algorithms. In this paper, two types of typical modulation signals in intentional modulation of radar signals, namely phase-coded (PSK) signals and frequency-modulated (FM) signals, are proposed. A SVEFD algorithm based on classification from coarse to fine. According to the characteristics of the 3 dB bandwidth of the frequency spectrum of the PSK signal and the FM signal, the coarse classification between the classes is performed first. Finally, through experimental simulations, the results show that the correct recognition probability of the SVEFD algorithm at a lower signal-to-noise ratio is much higher than the m algorithm and also higher than the SVD algorithm. When the signal-to-noise ratio is greater than 1 dB, the average recognition rate reaches 94%, which proves that The effectiveness of the proposed SVEFD algorithm. When the signal-to-noise ratio is .1 dB, in addition to maintaining the correct recognition rate of more than 90% for the LFM and COSTAS signals, the other six types of signals have low correct recognition rates, so the SVEFD algorithm has advantages in comparison.

随着电子技术的飞速发展及其在现代战争中的广泛应用，单个雷达信号源的调制方式更加灵活，参数多样，现代战场电磁信号环境复杂，使得基于传统雷达信号的识别成为可能。传统参数技术无法满足实际需求。在近年来出现的许多雷达信号调制识别算法中，神经网络理论和脉冲内分析算法被广泛使用，但是它们各自都有缺点，并且一种方法不能满足日益复杂的调制信号识别的需求。和参数估计需求。。基于上述背景，本文的研究内容是识别雷达信号的细微特征及其参数估计算法。本文提出了雷达信号的有意调制中的两种典型调制信号，即相位编码（PSK）信号和调频（FM）信号。基于从粗到细分类的SVEFD算法。根据PSK信号和FM信号的频谱的3 dB带宽的特性，首先进行类别之间的粗略分类。最后，通过实验仿真，结果表明，在较低的信噪比下，SVEFD算法的正确识别概率远高于m算法，也高于SVD算法。当信噪比大于1 dB时，平均识别率达到94％，证明了所提SVEFD算法的有效性。当信噪比为.1 dB时，除了对LFM和COSTAS信号的正确识别率保持超过90％之外，其他六种信号的正确识别率也很低，因此SVEFD算法具有比较优势。