识别空间旋转目标的尺度模型
Scaled Model for Recognizing the Spatial Rotating Target
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摘要: 由于空间目标旋转速率和雷达采样周期的不确定，雷达回波数据与模板数据存在着采样率不一致,影响匹配效果。该文通过多抽样率下自回归滑动平均(ARMA)混合模型建模，得到回波数据在模板尺度下的模型参数，实现同一尺度下的信号匹配。实验结果表明了该方法的有效性。Abstract: Considering the uncertain for rotating velocity of spatial target and sample period of radar observation, there exists sampling rate variance between radar returns and template data, which decreases the degree of matching. In this paper, the ARMA model in multirate is provided, through which the model parameters of radar turns in the scale of template data can be gotten, and then data can be matched in the same scale. Experimental result shows the validity of proposed method.
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宗孔德.多抽样率信号处理.北京:清华大学出版社,1996:21-31. ［2］Wei W W S, Stram D O. Disaggregation of time series models. J. Royal Stat. Soc., 1990, 52(2):453-467. ［3］Eom M B, Chellappa R. Noncooperative target classification using hierarchical modeling of highrange resolution radar signatures. IEEE Trans. on Signal Processing, 1997, 24(9): 2318-2327. 期刊类型引用(2)
1. 高锐锋，苗艳春，陈颖，王珏，张军，韩瑜，金石. 超大规模MIMO阵列可视区域空间分布数据集. 电子与信息学报. 2024(08): 3063-3072 . 
本站查看2. 张雷，杨帅，曾琦. 面向可视区域的超大规模MIMO低复杂度混合预编码. 电讯技术. 2024(12): 1931-1938 . 
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