用自适应损失函数提升非线性移动窗口估计的抗异常值能力。
Outlier-Robust Nonlinear Moving Horizon Estimation using Adaptive Loss Functions
- 引入可调损失函数，自动识别并降低异常数据影响。
- 仅需少数迭代即可完成自适应调整，性能优于传统L2方法。
- 适合含异常测量噪声的实时状态估计场景。
本文提出一种用于移动窗口估计（MHE）的自适应鲁棒损失函数框架，通过引入可调的鲁棒损失函数以降低异常值的影响，并加入正则项防止出现平凡解。该方法优先拟合无污染数据，同时对受污染数据进行降权处理。框架中引入一个调参项，用于控制损失函数形状，调节估计器对异常值的鲁棒性。仿真结果表明，该方法在数次迭代内即可实现自适应，而当测量无异常时，传统L2行为仍占主导地位。
原文摘要 · Abstract (English)
In this work, we propose an adaptive robust loss function framework for MHE, integrating an adaptive robust loss function to reduce the impact of outliers with a regularization term that avoids naive solutions. The proposed approach prioritizes the fitting of uncontaminated data and downweights the contaminated ones. A tuning parameter is incorporated into the framework to control the shape of the loss function for adjusting the estimator's robustness to outliers. The simulation results demonstrate that adaptation occurs in just a few iterations, whereas the traditional behaviour $\mathrm{L_2}$ predominates when the measurements are free of outliers.
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