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Velocity estimation of slow moving targets in AT-InSAR Systems

机译:insar系统缓慢移动目标的速度估计

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Along Track Interferometric Synthetic Aperture Radar (AT-InSAR) systems use more than one SAR antennas (typically two), mounted on the same platform and displaced along the platform moving direction, to detect slow ground moving targets. The phase of the ATI signal is related to the target motion parameters and may thus be used to estimate the radial velocity. In this paper we approach the velocity estimation problem using statistical techniques based on the statistical distribution of the measured interferometric phases. We analyze the radial velocity estimation with respect to ATI system parameters, such as velocity values, the signal to clutter ratio (SCR), the clutter to noise ratio (CNR), considering a deterministic target whose velocity is estimated using a Gaussian model. This model allows to take into account the lack of knowledge of the target radar cross section (RCS) values and provides an analytical form for the interferometric phase probability density function. Simulations results show that the adoption of Maximum Likelihood (ML) techniques, to perform a joint estimation of velocity and SCR, and multi-channel configurations, to overcome ambiguities problems, provide very good velocity estimation accuracy.
机译:沿着轨道干涉性合成孔径雷达(AT-INSAR)系统使用多于一个SAR天线(通常是两个),安装在同一平台上并沿平台移动方向移位,以检测慢接地的移动目标。 ATI信号的阶段与目标运动参数有关,因此可以用于估计径向速度。在本文中,我们使用基于测量的干涉阶段的统计分布使用统计技术来接近速度估计问题。考虑到使用高斯模型估计的确定性目标,我们分析关于ATI系统参数的径向速度估计,例如速度值,速度值,速度值,杂波比(SCR),对噪声比(CNR),对噪声比(CNR)进行噪声比(CNR)。该模型允许考虑目标雷达横截面(RCS)值的知识,并提供干涉相位概率密度函数的分析形式。仿真结果表明,采用最大似然(ML)技术,以执行速度和SCR的联合估计,以及多通道配置,以克服歧义问题,提供非常好的速度估计精度。

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