The micro-Doppler phenomenon in the echo signal received from a ballistic target (BT) with micro-motion is commonly used to discriminate BTs such as warheads and decoys. The joint time-frequency (JTF) analysis of the echo signal has been considered as useful two-dimensional (2-D) information in BT discrimination, which generally requires a framework based on the processing of the 2-D JTF image with various conventional feature extraction techniques. However, these techniques are inefficient for time-critical BT discrimination task due to the complicated 2-D image processing. In this paper, we propose new echo signal models to formulate the fundamental difference between the micro-motions of warheads and decoys, leading to a novel BT discrimination framework via new feature extraction paradigm and multi-aspect fusion concept. The most attractive attribute of this framework is that it can provide substantial savings with regard to computational resources as well as robustness to noise. The experimental results illustrate that the proposed discrimination scheme shows considerable promise for application in real-time BT discrimination.

中文翻译：

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使用微动有效识别弹道目标

从具有微运动的弹道目标 (BT) 接收到的回波信号中的微多普勒现象通常用于区分弹头和诱饵等 BT。回波信号的联合时频 (JTF) 分析一直被认为是 BT 鉴别中有用的二维 (2-D) 信息，这通常需要一个基于二维 JTF 图像处理的框架，具有各种常规特征提取技术。然而，由于复杂的二维图像处理，这些技术对于时间关键的 BT 鉴别任务效率低下。在本文中，我们提出了新的回波信号模型来表述弹头和诱饵的微运动之间的根本区别，通过新的特征提取范式和多方面融合概念导致了一种新的 BT 识别框架。该框架最吸引人的特性是它可以在计算资源和噪声鲁棒性方面提供大量节省。实验结果表明，所提出的鉴别方案在实时 BT 鉴别中显示出相当大的应用前景。