条件归一化流用于前向和后向联合状态与参数估计
Conditional Normalizing Flows for Forward and Backward Joint State and Parameter Estimation
AI总结：
针对非线性非高斯系统，提出基于条件归一化流的状态滤波方法，结合MLP、Transformer或Mamba-SSM生成条件嵌入，并引入最优传输动力学损失缓解过参数化，在自动驾驶和COVID-19联合估计中验证有效性。
摘要：
传统的状态估计滤波算法——如经典卡尔曼滤波、无迹卡尔曼滤波和粒子滤波——在应用于不确定性遵循任意非高斯且可能多峰分布的非线性系统时，性能会下降。本研究回顾了基于条件归一化流进行非线性滤波的状态估计最新方法，其中条件嵌入由标准MLP架构、Transformer或选择性状态空间模型（如Mamba-SSM）生成。此外，我们测试了最优传输启发的动力学损失项在缓解由大量变换组成的流中过参数化问题的有效性。我们研究了这些方法在自动驾驶和患者群体动力学相关应用中的性能，特别关注它们如何处理时间反转和链式预测。最后，我们评估了各种条件策略在真实世界COVID-19联合SIR系统预测和参数估计应用中的性能。
英文摘要：
Traditional filtering algorithms for state estimation -- such as classical Kalman filtering, unscented Kalman filtering, and particle filters -- show performance degradation when applied to nonlinear systems whose uncertainty follows arbitrary non-Gaussian, and potentially multi-modal distributions. This study reviews recent approaches to state estimation via nonlinear filtering based on conditional normalizing flows, where the conditional embedding is generated by standard MLP architectures, transformers or selective state-space models (like Mamba-SSM). In addition, we test the effectiveness of an optimal-transport-inspired kinetic loss term in mitigating overparameterization in flows consisting of a large collection of transformations. We investigate the performance of these approaches on applications relevant to autonomous driving and patient population dynamics, paying special attention to how they handle time inversion and chained predictions. Finally, we assess the performance of various conditioning strategies for an application to real-world COVID-19 joint SIR system forecasting and parameter estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
