基于积谱的雷达目标高分辨率距离像识别

Product spectrum based target recognition using high resolution radar range profiles

针对飞机目标的识别问题,将一种用于语音识别中的积谱特征引入雷达目标一维距离像识别领域.积谱定义为功率谱与群延迟的乘积,该特征能够充分利用目标一维距离像的幅度谱与相位谱信息.在目标分类阶段,选择基于弹性传播(RPROP)算法的多层前馈神经网络作为分类器.利用4种飞机模型的重点散射源二维分布测试数据和频率步进法得到目标的一维纵向距离像,对距离像积谱的分类性能进行了测试,结果表明基于积谱的特征对于一维距离像具有较高的识别率,并具有较好的抗噪性能.

To solve the problem of classifying the aircrafts using high resolution radar range profiles, the product spectrum which was used in the speech signal processing community was introduced to the radar target recognition community. The product spectrum was defined as the product of the power spectrum and the group delay, this feature combined the information contained in the magnitude spectrum and the phase spectrum of the range profiles and carried more information about the shape of the aircrafts. A multi-layered feed-forward neural network with resilient propagation （RPROP） algorithm was selected as classifier. The range profiles were obtained by step-frequency technique using the two-dimension backscatters distribution data of four different aircraft models. Simulations were presented to evaluate the classification performance with the product spectrum features. The results show that the product spectrum based features can yield good performance even in noisy conditions for the application of radar target recognition.