雷达脉冲重复间隔(PRI)的调制类型是分析雷达工作状态和任务的重要手段。针对常见PRI 调制类型识别算法无法识别未知调制类型的问题，文中提出一种基于改进极值机(EVM)的雷达PRI 调制类型开集识别方法。首先，采用残差-双向长短时记忆网络进行PRI 序列的特征提取；其次，结合原型学习,利用基于距离的交叉熵损失和原型损失对特征提取网络进行训练；最后，在特征空间中引入已知类特征的线性组合以模仿未知类的行为，提出了改进的EVM 模型。实验结果表明，与EVM 相比,文中所提方法能够提升雷达PRI 调制类型的识别准确率，且在开放的电磁环境下具有良好的开集适应性。波器。首先,基于有限集统计理论,利用CPHD 滤波器建立多扩展目标的贝叶斯滤波框架;然后,采用ERHM 描述扩展目标的量测源分布,并利用无迹变换嵌入CPHD 滤波流程;最后,仿真实验结果表明,ERHM-CPHD 滤波器对椭圆扩展目标的跟踪性能优于传统的伽马高斯逆威沙特CPHD 滤波器,在杂波密度较高、目标新生的位置比较确定的场景或者扩展目标数目较多时,对扩展目标的参数估计更为准确。所提方法在高分辨率雷达多目标跟踪方面具备很好的运用前景。

The modulation type of the radar pulse repetition interval (PRI) is an important means to analyze the working state and task of the radar. In order to solve the problem that common radar PRI modulation type recognition algorithms cannot identify unknown modulation types, an open-set recognition method for radar PRI modulation types based on improved extreme value machine (EVM) is proposed. Firstly, the residual network and bi-directional long short-term memory network is used to extract the features of PRI sequence. Secondly, combined with prototype learning, the feature extraction network is trained by distance-based cross-entropy loss and prototype loss. Finally, an improved EVM model is proposed by introducing a linear combination of known class features into the feature space to simulate the behavior of unknown classes. Experimental results show that compared with EVM, the proposed method can improve the recognition accuracy of radar PRI modulation type, and has good open-set adaptability in open electromagnetic environment.

TN957.51

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