| 摘要: |
| 雷达采样数据率及统计RCS特征的滑窗参数（滑动步长和窗口长度）需要设定以实现基于RCS统计特征的目标识别。利用类内/类间散布矩阵构造了类别可分性的距离判据，分析了数据率和滑窗参数对不同目标的可分性影响。结果表明：随着数据率提升，目标可分性呈现在低频区域（<5Hz）快速增长，而在高频区域(>25Hz)呈现缓慢增长趋于饱和；目标可分性随滑动步长变化不显著；而目标可分性随着窗口长度的增加呈现非线性的快速增长。 |
| 关键词: 目标识别 RCS统计特征 类别可分性判据 采样数据率 滑窗参数 |
| DOI：DOI:10.3969/j.issn.1672-2337.2020.02.015 |
| 分类号:TN95 |
| 基金项目: |
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| Influences of Sampling Frequency and Parameters of Sliding Window on Radar Target Recognition Using Radar Cross Section Features |
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WANG Langning，HOU Yanpan，LI Yanfeng
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Taiyuan Satellite Launch Center，Taiyuan 030027，China
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| Abstract: |
| In order to classify the radar target by the characteristics of radar cross section (RCS) data，the sampling frequency and the parameters of sliding window (window length and sliding step) need to be determined firstly. On the basis of traditional sorted scatter matrix theory，the distance separability criterion is used as the quantification factors to analyze the influences of parameters on the separability. Experimental results of the RCS data demonstrate that，under a low sampling frequency region the separability of RCS data increase quickly with the frequency growing，while under a high frequency region the growth of separability is quite low and approaches to saturation. As the influences of the sliding window parameters during calculating the RCS features，the sliding step has no effect on the separability，while the separability shows a nonlinear fast growth with the window length increasing. |
| Key words: radar target recognition radar cross section (RCS) separability criterion sampling frequency sliding window parameter |
