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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series
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A Sequential Monte Carlo Framework for Noise Filtering in InSAR Time Series

机译：Insar时间序列中噪声滤波的顺序蒙特卡罗框架

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摘要

This article proposes an alternative filtering technique to improve interferometric synthetic aperture radar (InSAR) time series by reducing residual noise while retaining the ground deformation signal. To this end, for the first time, a data-driven approach is introduced, which is based on Takens's method within the sequential Monte Carlo framework, allowing for a model-free approach to filter noisy data. Both a Kalman-based filter and a particle filter (PF) are applied within this framework to investigate their impact on retrieving the signals. More specifically, PF and particle smoother [PaSm; to avoid confusion with persistent scatterers (PSs)] are tested for their ability to deal with non-Gaussian noise. A synthetic test based on simulated InSAR time series, as well as a real test, is designed to investigate the capability of the proposed approach compared with the spatiotemporal filtering of InSAR time series. Results indicate that PFs and more specifically PaSm perform better than other applied methods, as indicated by reduced errors in both tests. Two other variants of PF and adaptive unscented Kalman filter (AUKF) are presented and are found to be able to perform similar to PaSm but with reduced computation time. This article suggests that PFs tested here could be applied in InSAR processing chains.
机译：本文提出了一种替代的过滤技术，以通过降低保持地变形信号的同时降低残留噪声来改善干涉性合成孔径雷达（INSAR）时间序列。为此，介绍了第一次数据驱动的方法，该方法是基于所持蒙特卡罗框架内的Takens的方法，允许无模型方法过滤噪声数据。在该框架内应用基于卡尔曼的滤波器和粒子滤波器（PF），以研究它们对检索信号的影响。更具体地，PF和颗粒更平滑[PASM;为避免与持久散射体（PSS）混淆，以进行处理非高斯噪声的能力。基于模拟的INSAR时间序列以及真实测试的合成测试旨在研究所提出的方法的能力与INSAR时间序列的时空滤波相比。结果表明，PFS和更具体地的PASM比其他应用方法更好，如两种测试中的误差降低所示。呈现了另外两种PF和自适应无需卡尔曼滤波器（AukF）的变体，并发现能够执行类似于PASM但具有降低的计算时间。本文表明，这里测试的PFS可以应用于Insar处理链。

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