用学习方法自动识别噪声特性，提升车辆状态估计精度
Physics constrained learning of stochastic characteristics
- 基于创新序列设计多种损失函数，学习噪声统计特性
- 在真实车辆数据上验证，显著改善滤波器稳定性与估计误差
- 适合需要高可靠性状态估计的自动驾驶系统开发者
精确的状态估计算法需准确处理过程与测量模型中的不确定性，而这些噪声特性通常未知，需依赖经验选择协方差矩阵。错误的矩阵选择会降低估计精度，甚至导致滤波发散。由于噪声源不确定且难以系统建模，噪声特性识别长期面临挑战。现有方法多通过优化创新序列来推断未知协方差矩阵。近年来，学习方法被用于识别过程与测量模型的随机特性。本文提出一种基于学习的框架，采用不同损失函数识别噪声特性，并在真实车辆状态估计任务中测试其性能。
原文摘要 · Abstract (English)
Accurate state estimation requires careful consideration of uncertainty surrounding the process and measurement models; these characteristics are usually not well-known and need an experienced designer to select the covariance matrices. An error in the selection of covariance matrices could impact the accuracy of the estimation algorithm and may sometimes cause the filter to diverge. Identifying noise characteristics has long been a challenging problem due to uncertainty surrounding noise sources and difficulties in systematic noise modeling. Most existing approaches try identifying unknown covariance matrices through an optimization algorithm involving innovation sequences. In recent years, learning approaches have been utilized to determine the stochastic characteristics of process and measurement models. We present a learning-based methodology with different loss functions to identify noise characteristics and test these approaches' performance for real-time vehicle state estimation
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