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本文已经指出,在共振区域中的雷达回波载有目标的全部尺寸和形状的信息。文中讨论了利用此种回波而发展的两种目标分类技术。这两种技术利用了共振区域中雷达截面(RCS)的后向散射测量和目标固有的后向散射相位。用来对这些技术进行试验的目标一览表,是根据测量按比例缩小模型的现代飞机和舰船的雷达截面而得出的,这些飞机和舰船正处于俄亥俄州立大学的雷达的作用距离内。为了试验分类技术,这些目标有它们从数据库取得的雷达截面和相位,由于模拟全尺寸传播路径及处理失真的误差而使这些特征发生差错,因此采用了若干种分类方法来确定怎样使这些有差错的测量结果与分类目录上的测量目标特征值较好吻合。对于雷达截面的大小和工作频率点数(例如2、4或8)时的(经距离校正的)相位,第一种技术使用了最邻近(NN)算法。第二种技术采用将复杂的多频雷达回波作傅里叶逆变换成时间域,接着进行互相关的方法。对各种处理的选择方案,文中把作为信号一误差噪声功率比的函数的两种技术的性能作了比较。
It has been pointed out here that the radar echoes in the resonance region carry information on the full size and shape of the target. In the paper, two kinds of target classification technologies developed by using such echoes are discussed. Both techniques make use of backscatter measurements of the radar cross section (RCS) in the resonance region and the target’s inherent backscatter phase. The list of targets used to test these technologies is based on a radar cross section measuring modern aircraft and vessels that scale down the models, which are within range of the radar at Ohio State University. To test the classification technique, these targets have their radar cross-section and phase acquired from the database, and as a result of errors in simulating full-size propagation paths and handling of distortions that make these features faulty, several classification methods are used to determine how to make these errors The measured results with the classification of the target value of the measured target better agreement. The first technique uses the nearest neighbor (NN) algorithm for the (distance-corrected) phase of the radar cross-section size and number of operating frequencies (eg, 2, 4, or 8) The second technique uses a method of inverse Fourier transform of complex multi-frequency radar echoes into the time domain followed by cross-correlation. The performance of the two techniques as a function of the signal-to-noise power ratio is compared for various processing options.