Chirplet-based target recognition using RADAR technology

M. Alaee, H. Amindavar
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引用次数: 13

Abstract

In this paper, q-chirplet based signal processing is applied to data from a low-resolution ground surveillance pulse Doppler RADAR, to classify three classes of targets: personnel, wheeled vehicles and animals. We utilize Zernike moments (ZM) over the chirplet parameters to determine the pertinent features. Our work provides a new approach for multiresolution analysis and classification of non-stationary signals with the objective of revealing important features in an unknown noise and clutter environment. The algorithm is trained and tested on real RADAR signatures of multiple examples of moving targets from each class. The results show the proposed algorithm invariancy against speed and orientation of the targets.
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基于chirplet的雷达目标识别技术
本文将基于q-chirplet的信号处理应用于低分辨率脉冲多普勒地面监视雷达数据,对人员、轮式车辆和动物三类目标进行分类。我们利用小波参数上的泽尼克矩(ZM)来确定相关特征。我们的工作为非平稳信号的多分辨率分析和分类提供了一种新的方法,目的是揭示未知噪声和杂波环境中的重要特征。算法在每个类别的多个运动目标的真实雷达特征上进行了训练和测试。结果表明,该算法对目标的速度和方向具有不变性。
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