宽带雷达光学区频域识别法

该文以宽带雷达光学区目标识别为背景,由频域测量数据构造了不随目标距离像沿径向平移而改变的频域波形回波幅值波形和相位特征波形;基于此波形,提取了两种对目标方位角不敏感的识别特征广义频数和波形长度;并借助于时频分析中尺度变换的概念,把特征集进一步完备化。针对频域直接识别法易受测量噪声影响的缺点,设计了相应的预处理算法。选用FMM神经网络作为分类器,并修改了它传统的学习算法。对5种喷气飞机模型的识别结果表明,该算法具有较高的正确识别率。

FREQUENCY-DOMAIN RECOGNITION METHOD FOR WIDEBAND RADAR OPTICAL REGION TARGET

Meeting the application requirements of wideband radar optical region target recognition, this paper presents a simple and effective frequency-domain recognition method. First, two kinds of waves called backscattering amplitude wave and phase feature wave are constructed directly from frequency measured data sets, which keep invariant on the shift of target in the radial direction. Based on these waves, generalized frequency and length of wave are extracted as recognition features insensitive to target azimuth. With the aid of the idea of "ruler transform" in time-frequency analysis, the feature sets are further completed. Aiming at lessening the effect of measuring noise, the paper then designs a specific preprocessing method. FMM neural network is chosen as the classifier with modified training algorithm. The recognition results show that this target recognition algorithm can obtain high correct classification rate.