一种基于Mellin匹配滤波的运动目标速度估计算法

A. Monakov
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引用次数: 0

摘要

介绍。在合成孔径雷达(SAR)中,运动目标的雷达图像构造和速度估计是一个相关的研究问题。雷达成像的低质量往往与距离元迁移现象有关。传统的RCM补偿方法可以成功地用于获取静止目标的雷达图像,但当应用于运动目标时,却不能提供所需的质量。目前,有许多算法被用来解决这个问题。然而,它们中的大多数在搜索未知参数的估计时使用优化过程,这实际上使它们的实现变得非常复杂。一个例外是LvD算法,它实现双梯形变换来构造雷达图像,而不使用复杂的估计搜索过程。雷达图像以“纵向速度-横向速度”坐标构造,便于估计目标速度分量。基于Mellin匹配滤波(MMF)的侧视sar运动目标速度估计和雷达图像构建替代算法的发展。材料与方法该算法基于积分Mellin变换对信号尺度的不变性,利用MMF估计目标速度分量。综合了一种基于MMF构造运动目标雷达图像的算法。对LvD算法的分析表明,在实现第二次KT时,LvD算法具有选择最佳比例因子的能力。对MMF算法和LvD算法进行了计算机仿真,结果表明它们具有相同的精度。在相同的仿真场景下,当信噪比大于-10 db时,两种算法都能有效估计出运动目标的速度分量。所提出的雷达图像构造算法可用于运动目标探测和速度估计的SAR系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Algorithm for Estimating the Velocity of a Moving Target Based on Mellin Matched Filter
Introduction. Construction of the radar image of a moving target and estimation of its velocity in synthetic aperture radars (SAR) presents a relevant research problem. The low quality of radar imaging is frequently related to the phenomenon of range cell migration (RCM). Conventional methods for RCM compensation, which are successfully used to obtain radar images of stationary targets, fail to provide the required quality when applied to moving targets. At present, a number of algorithms are used to solve this problem. However, the majority of them employ optimization procedures when searching for estimates of unknown parameters, which fact greatly complicates their implementation. An exception is the LvD algorithm, which implements double keystone transform to construct a radar image without using complex estimate search procedures. Radar images are constructed in the coordinates "longitudinal velocity - lateral velocity", which facilitates estimation of the target velocity components.Aim. Development of an alternative algorithm based on the Mellin matched filter (MMF) for estimating the velocity and constructing the radar image of a moving target in a side-looking SAR.Materials and methods. The derived algorithm is based on the invariance of the integral Mellin transform to the signal scale and uses the MMF to estimate the target velocity components.Results. An algorithm for constructing the radar image of a moving target based on the MMF was synthesized. An analysis of the LvD algorithm showed its capacity for selecting the optimum scale factor when implementing a second KT. The conducted computer simulation of the MMF and LvD algorithms showed their equal accuracy. Under the same simulation scenarios, both algorithms yield effective estimates of the velocity components of a moving target when the signal-to-noise ratio is greater than -10 dB.Conclusion. The proposed algorithm for constructing a radar image can be used in SAR systems designed for detection and velocity estimation of a moving target.
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