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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立叶独立分量分析

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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:区分具有多个运动部件的雷达目标的能力已成为航空航天和地面目标识别和分析的极大兴趣。尤其是直升机和带有旋翼的其他目标,例如微型无人机,在雷达回波中表现出独特的特征,可用于识别它们。本文利用独立分量分析(ICA)的信号分离能力,提出了一种解决微多普勒签名分解问题的新算法。该算法的核心是通过使用ICA程序来精确表示的,该程序已被证明是一种用于分离观察结果混合中的隐藏信息的非常有效的技术。 ICA已成功应用于多种应用中,例如无线通信,雷达波束成形,痕量气体分解和医学成像处理。以多静态雷达系统中直升机的旋翼桨叶签名分解为案例研究,通过将ICA应用到模拟的多分量微多普勒签名中获得的结果表明,该方法具有成功完成分解操作的能力。

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