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Time-Varying SAR Interference Suppression Based on Delay-Doppler Iterative Decomposition Algorithm

机译:基于延迟多普勒迭代分解算法的时变SAR干扰抑制

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摘要

Narrow-band interference (NBI) and Wide-band interference (WBI) are critical issues for synthetic aperture radar (SAR), which degrades the imaging quality severely. Since some complex signals can be modeled as linear frequency modulated (LFM) signals within a short time, LFM-WBI and NBI are mainly discussed in this paper. Due to its excellent energy concentration and useful properties (i.e., auto-terms pass through the origin of Delay-Doppler plane while cross-terms are away from it), a novel nonparametric interference suppression method using Delay-Doppler iterative decomposition algorithm is proposed. This algorithm consists of three stages. First, we present signal synthesis method (SSM) from ambiguity function (AF) and cross ambiguity function (CAF) based on the matrix rearrangement and eigenvalue decomposition. Compared with traditional SSM from Wigner distribution (WD), the proposed SSM can synthesize a signal faster and more accurately. Then, based on unique properties in Delay-Doppler domain, a mask algorithm is applied for interference identification and extraction using Radon and its inverse transformation. Finally, a signal iterative decomposition algorithm (IDA) is utilized to subtract the largest interference from the received signal one by one. After that, a well-focused SAR imagery is obtained by conventional imaging methods. The simulation and measured data results demonstrate that the proposed algorithm not only suppresses interference efficiently but also preserves the useful information as much as possible.
机译:窄带干扰(NBI)和宽带干扰(WBI)是合成孔径雷达(SAR)的关键问题,它严重降低了成像质量。由于一些复杂的信号可以在短时间内建模为线性调频(LFM)信号,因此本文主要讨论LFM-WBI和NBI。由于其出色的能量集中度和有用的性能(即自动项通过延迟多普勒平面的原点而交叉项远离其),提出了一种使用延迟多普勒迭代分解算法的新型非参数干扰抑制方法。该算法包括三个阶段。首先,我们基于矩阵重排和特征值分解,从模糊函数(AF)和交叉歧义函数(CAF)提出信号合成方法(SSM)。与来自Wigner Distribution(WD)的传统SSM相比,该SSM可以更快,更准确地合成信号。然后,根据Delay-Doppler域中的唯一属性,使用Radon及其逆变换将掩码算法应用于干扰识别和提取。最后,信号迭代分解算法(IDA)用于从接收信号中一一减去最大干扰。之后,通过常规成像方法获得聚焦良好的SAR图像。仿真和实测数据结果表明,该算法不仅有效地抑制了干扰,而且还尽可能地保留了有用的信息。

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