无监督学习是解决未知雷达辐射源信号识别的有效方法。
Unsupervised learning is a good method to solve the problem of recognition of unknown radar emitter signal.
对未知雷达辐射源信号进行准确分选是当前电子对抗领域迫需解决的一个难题。
It is a difficult problem to sorting unknown radar emitter in the electronic countermeasure field at present.
雷达辐射源信号识别是衡量雷达对抗设备技术先进程度的重要标志，是现代电子对抗领域急需解决的难题。
Recognition of radar emitter signals is an important symbol to measure the technical level of radar countermeasure in modern electronic countermeasure, besides, it is an urgent problem to solve.
雷达辐射源信号分选是电子侦察信号处理的关键环节，直接影响着电子侦察设备性能的发挥并关系到战争的后续作战决策。
The deinterleaving of radar emitter signals is a crucial technique in signal processing of electronic Intelligence, which directly determines the performance of electronic reconnaissance equipment.
相反，在研究雷达辐射源信号无意调制时，常规参数信息不再是研究的主体，体现雷达辐射源个体固有属性的无意调制特征成为关注的重点。
On the contrary, unintentional modulation that reflects the inherent properties of radar emitter is the emphasis when studying the unintentional modulation of radar emitter signal.
针对雷达对抗信号处理研究工作中的关键问题，本文研究了基于循环平稳分析的雷达辐射源特征提取与融合识别。
Aiming at the key issue of electronic warfare, feature extraction and fusion recognition of radar emitters based on cyclostationary analysis are proposed in this dissertation.
最后再对融合后的信号特征按最大相关准则，从雷达辐射源知识库中找出要协同识别的雷达型号。
Finally, compared to the radar type of radar recognition database according to the maximum correlation criterion, and then elicited the right radar type that waiting for recognizes.
分析、对比了调频信号、模拟电视信号、数字电视信号模糊函数的的特点，并针对数字电视信号，分析和研究了外辐射源雷达系统的关键技术。
In this paper, the features of signal ambiguity function for FM, TV and DTV are listed and analyzed. The key techniques of passive radar system based on DTV signal are studied and analyzed.
对雷达辐射源进行准确识别是当前研究的重点课题之一，主要包括对雷达辐射源个体信号、雷达辐射源型号的识别。
It is important to recognize radar emitters at present. The recognition consists of radar individual signal recognition and radar model recognition.
对雷达辐射源进行准确识别是当前研究的重点课题之一，主要包括对雷达辐射源个体信号、雷达辐射源型号的识别。
It is important to recognize radar emitters at present. The recognition consists of radar individual signal recognition and radar model recognition.
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