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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Using Residual Resampling and Sensitivity Analysis to Improve Particle Filter Data Assimilation Accuracy
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Using Residual Resampling and Sensitivity Analysis to Improve Particle Filter Data Assimilation Accuracy

机译：使用残留重采样和灵敏度分析提高粒子滤波数据同化精度

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摘要

Data assimilation (DA), an effective approach to merge dynamic model and observations to improve states estimation accuracy, has been a hot topic in the earth science and lots of efforts have been devoted to the DA algorithms. In this paper, an improved residual resampling particle filtering (improved RR-PF) is proposed. Compared with the generic residual resampling particle filtering (generic RR-PF), the improved RR-PF not only solves the degradation of particles, but also maintains the diversity of particles. Besides, sensitivity analysis is carried out to analyze the impact of some parameters to assimilation and to determine the optimal parameters. These parameters are of significant importance to DA but cannot be determined easily. Finally, soil moisture from Soil Moisture Experiment 2003 and VIC model simulations were assimilated with the improved RR-PF with parameters determined by the sensitivity analysis. The result shows that the accuracy of soil moisture greatly improves after DA. Compared with generic RR-PF, the performance of improved RR-PF is superior in accuracy and diversity of particles.
机译：数据同化（DA）是一种将动态模型与观测结果相结合以提高状态估计精度的有效方法，已成为地球科学领域的热门话题，并且已经为DA算法做出了很多努力。本文提出了一种改进的残差重采样粒子滤波（改进的RR-PF）。与通用残差重采样粒子滤波（通用RR-PF）相比，改进后的RR-PF不仅解决了粒子的退化问题，而且还保持了粒子的多样性。此外，还进行了敏感性分析，以分析某些参数对同化的影响并确定最佳参数。这些参数对DA至关重要，但无法轻松确定。最后，将改进的RR-PF与土壤水分实验2003和VIC模型模拟中的土壤水分同化，并通过敏感性分析确定参数。结果表明，DA处理后土壤水分的准确性大大提高。与普通的RR-PF相比，改进后的RR-PF的性能在粒子的准确性和多样性上更为出色。

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