Mathematically describing the physical process of a sequential data assimilation system perfectly is difficult and inevitably results in errors in the assimilation model. Filter divergence is a common phenomenon because of model inaccuracies and affects the quality of the assimilation results in sequential data assimilation systems. In this study, an approach based on an L₁-norm constraint for filter-divergence suppression in sequential data assimilation systems was proposed. The method adjusts the weights of the state-simulated values and measurements based on new measurements using an L₁-norm constraint when filter divergence is about to occur. Results for simulation data and real-world traffic flow measurements collected from a sub-area of the highway between Leeds and Sheffield, England, showed that the proposed method produced a higher assimilation accuracy than the other filter-divergence suppression methods. This indicates the effectiveness of the proposed approach based on the L₁-norm constraint for filter-divergence suppression.

中文翻译：

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序列数据同化系统中滤波器发散抑制的一种方法及其在短期交通流量预测中的应用

用数学方法完美描述顺序数据同化系统的物理过程非常困难，并且不可避免地会导致同化模型出错。由于模型的不准确性，过滤器发散是一种常见现象，它会影响顺序数据同化系统中同化结果的质量。在这项研究中，提出了一种基于L ₁范数约束的方法，用于在顺序数据同化系统中抑制滤波器发散。该方法基于新的测量值使用L ₁来调整状态模拟值和测量值的权重-norm约束，即将发生过滤器发散。从英格兰利兹和谢菲尔德之间的高速公路子区域收集的仿真数据和实际交通流量测量结果表明，与其他滤波器发散抑制方法相比，该方法产生的同化精度更高。这表明基于L _1-范数约束的拟议方法对于滤波器发散抑制的有效性。