| 摘要: |
| 针对现有特征量对抗新型垂直极化箔条干扰效果差的问题，对现有极化角均值特征量进行了修正，选取最小极化角均值特征量作为新的识别特征量，对新的识别特征量进行了特性分析，给出了该特征量随雷达入射余角、雷达入射波长、箔条姿态的变化规律，并基于箔条回波数据验证了上述变化规律的正确性和可靠性。然后，利用上述特性分析的结果以及箔条干扰和舰船回波在最小极化角均值特征量取值上的差异，采用可分性测度函数，定量分析了该特征量在区分真假目标时的可分性性能。研究结果表明：最小极化角均值特征量克服了现有特征量对箔条空间取向分布、雷达入射余角等参数敏感的缺点，提高了区分真假目标时的稳健性和鲁棒性。 |
| 关键词: 垂直极化箔条 特征量 特性分析 可分性性能 |
| DOI：DOI:10.3969/j.issn.1672-2337.2020.02.011 |
| 分类号:TN957；TN972 |
| 基金项目: |
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| Chaff Interference Recognition Method Based on Polarization Characteristic Analysis |
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JIAO Dandan，LIU Zheng，LYU Fangfang，ZHANG Yanyan
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National Key Laboratory of Radar Signal Processing，Xidian University，Xian 710071，China
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| Abstract: |
| Aiming at the problem that the existing characteristic quantities have poor interference effect against the new vertical polarization chaff，this paper modifies the characteristic quantity of the existing polarization angle by selecting the value of the minimum polarization angle as the new identification characteristic quantity. Then，the new identification characteristic quantity is analyzed and the change laws of the characteristic quantity with the variations of the incident angle of the radar，the incident wavelength of the radar and the attitude of the chaff are given. The correctness and reliability of the above change laws are proved based on the chaff echo data. Finally，the separable properties for distinguishing the true and false targets are quantitatively analyzed by using the separable measure function and the results of the above characteristic analysis，as well as the difference between the chaff interference and the ship echo in the minimum polarization angle mean feature value. The result shows that the minimum polarization angle characteristic quantity overcomes the shortcomings of the existing characteristic quantities sensitive to the spatial orientation distribution of the chaff and the incident angle of the radar，and improves the robustness of the distinguishing between true and false targets. |
| Key words: vertical polarization chaff characteristic quantities characteristic analysis separable properties |
