Method for scatterer trajectory association of sequential ISAR images based on Markov chain Monte Carlo algorithm
Method for scatterer trajectory association of sequential ISAR images based on Markov chain Monte Carlo algorithm
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DOI：
10.1049/iet-rsn.2018.5180
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Lei Liu;Feng Zhou;Xueru Bai
中科院分区：
文献类型：
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作者：
Lei Liu;Feng Zhou;Xueru Bai
基于序列逆合成孔径雷达（ISAR）图像，利用因式分解方法可以重建目标的三维结构。然而，它需要精确的散射体轨迹形成，这是困难的，由于闭塞和轨迹交叉。针对这一问题，提出了一种基于马尔可夫链蒙特卡罗（MCMC）算法的散射体轨迹关联方法。首先，推导了目标在静止旋转运动模型下各散射体运动轨迹的椭圆运动特性。然后，通过计算压缩后回波的信噪比，利用旋转不变性技术对信号参数进行二维估计，可以准确有效地提取出每幅ISAR图像中散射体的数量和位置。接下来，他们提出了散射体轨迹关联问题的贝叶斯模型和推理算法。MCMC被用来估计散射体轨迹矩阵。特别地，他们利用每个散射体轨迹的椭圆运动特性，在MCMC中设计了新的先验和似然评价准则。仿真实验结果验证了该方法的有效性。
Based on sequential inverse synthetic aperture radar (ISAR) images, the three-dimensional target structure can be reconstructed using the factorisation method. However, it requires accurate scatterer trajectory formation, which is difficult due to the occlusion and trajectory crossing. To address this problem, the authors propose a novel scatterer trajectory association method based on Markov chain Monte Carlo (MCMC) algorithm. First, they derive the ellipse movement characteristics of each scatterer trajectory under stationary rotational motion model of the observed target. Then, by computing the signal-to-noise ratio of the compressed echoes, the number and positions of the scatterers in each ISAR image can be extracted precisely and efficiently through two-dimensional estimation of signal parameters via rotational invariance techniques. Next, they present a Bayesian model and inference algorithm for the scatterer trajectory association problem. MCMC is applied to estimate the scatterer trajectory matrix. Particularly, they design new prior and likelihood evaluation criterions in MCMC by making use of the ellipse movement characteristics of each scatterer trajectory. Experimental results on simulated data validate the effectiveness of the proposed method.