ISAR机动目标的平动补偿和瞬时成像研究

Study of Translational Motion Compensation and Instantaneous Imaging of ISAR Maneuvering Target

机动目标成像近年来受到广泛注意 .本文首先讨论平动补偿 ,它通常可以分解为两步进行──包络对齐和自聚焦 ,分析表明 ,针对平稳目标的包络对齐方法仍然适用于机动目标 ,而根据相干积累原理 ,已有的自聚焦方法从理论上和实际上都不是最优的 ,我们提出适用于机动目标和平稳目标的迭代相干积累自聚焦 (ICSA)算法 ,PGA(相位梯度算法 )是ICSA算法的一个特例 ;然后 ,本文讨论机动目标的瞬时成像 ,它实际上是一个瞬时谱估计问题 ,已有的一些瞬时成像方法只适用于散射点子回波为线性调频信号 (多普勒分布为直线 ) .针对时频分布为非直线的情况 ,我们提出用自适应窗短时chirplet分解方法估计信号的瞬时频率和瞬时幅度 ,并结合“洁净”技术 ,提出了快速自适应窗短时chirplet分解成像 (ACDI)算法 .

For inverse Synthetic Aperture Radar (ISAR) imaging of maneuvering target, we first discuss translational motion compensation (TMC), which is usually decomposed into two steps: envelope alignment and autofocus, and find that the existing envelope alignment algorithms are still effective for not too big maneuvering target, but the existing autofocus algorithms are not optimum for maneuvering target. We propose an iterative coherent summation autofocus (ICSA) algorithm, with PGA being a particular case of ICSA. Then we discuss maneuvering target imaging problem, which is an instantaneous spectrum estimation problem. Most existing algorithms are only effective when scatterers' echoes are linear frequency modulation (LFM) signals. When scatterers' time-frequency distribution is not linear, we put forward an adaptive chirplet decomposition method to estimate instantaneous frequency and amplitude of multi-component polynomial phase signals, and propose a fast adaptive chirplet decomposition imaging (ACDI) algorithm by utilizing 'clean' technique. Real data processing proves that the proposed ICSA algorithm and ACDI algorithm are effective.