标题： 状态空间模型中的截断高斯噪声估计 显示英文标题

标题： Truncated Gaussian Noise Estimation in State-Space Models

摘要： 在贝叶斯状态估计中，已经投入了大量努力将约束条件纳入状态估计，以用于过程优化、状态监控、故障检测和控制。 然而，在状态空间系统识别领域，普遍的做法是在高斯噪声假设下构建模型，这在噪声服从有界分布时可能导致不准确。 为了将高斯噪声假设推广到可能的截断密度，本文引入了一种在受截断高斯噪声影响的状态空间模型中估计噪声参数的方法。 我们提出的数据驱动方法基于最大似然原理与期望最大化算法的结合。 所提出方法的有效性得到了一个仿真示例的支持。 显示英文摘要

摘要： Within Bayesian state estimation, considerable effort has been devoted to incorporating constraints into state estimation for process optimization, state monitoring, fault detection and control. Nonetheless, in the domain of state-space system identification, the prevalent practice entails constructing models under Gaussian noise assumptions, which can lead to inaccuracies when the noise follows bounded distributions. With the aim of generalizing the Gaussian noise assumption to potentially truncated densities, this paper introduces a method for estimating the noise parameters in a state-space model subject to truncated Gaussian noise. Our proposed data-driven approach is rooted in maximum likelihood principles combined with the Expectation-Maximization algorithm. The efficacy of the proposed approach is supported by a simulation example.