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Fourier independent component analysis of radar micro-Doppler features

机译:雷达微多普勒特征的傅里叶独立分量分析

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The capability of discriminating radar targets exhibiting multiple moving parts has become of great interest for both aerospace and ground-based target recognition and analysis. In particular, helicopters and other targets with rotors, as for instance miniature Unmanned Aerial Vehicles, exhibit peculiar characteristics in the radar return that can be used for their recognition. In this paper a novel algorithm to address the problem of micro-Doppler signature unmixing is proposed, exploiting the signal separation capabilities of the Independent Component Analysis (ICA). The core of the algorithm is represented precisely by the use of the ICA procedure, that has been already proved to be a very effective technique for separating hidden information in mixtures of observations. ICA has been successfully employed in several applications such as wireless communications, radar beamforming, trace-gases unmixing and medical imaging processing. The helicopter's rotor blade signature unmixing from a multi-static radar system is considered as case study and results obtained through the application of ICA to simulated multi-component micro-Doppler signatures show the capability of the proposed approach to successfully accomplish the unmixing operation.
机译:鉴别呈现多个移动部件的雷达靶的能力对航空航天和基于地面的目标识别和分析具有很大的兴趣。特别地,具有转子的直升机和其他目标,例如微型无人驾驶车辆,在雷达返回中展示了可用于其识别的雷达返回的特殊特征。本文提出了一种解决微多普勒特色解密问题的新算法,利用独立分量分析(ICA)的信号分离能力。算法的核心是通过使用ICA程序来表示的,这已经被证明是一种非常有效的技术,用于将隐藏信息分离在观察的混合物中。 ICA已成功使用在多种应用中,例如无线通信,雷达波束形成,痕量气体解密和医学成像处理。从多静态雷达系统中解密的直升机的转子叶片特征被认为是通过应用ICA到模拟的多组分微多普勒签名而获得的案例研究和结果显示了所提出的方法成功完成解密操作的方法。

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