论文标题
在部分观察到的马尔可夫过程中，可变的分裂方法用于约束状态估计
Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes
论文作者
论文摘要
在本文中，我们提出了一类有效，准确且一般的方法，用于解决平等和不平等约束的状态估计问题。这些方法基于可变分裂和部分观察到的马尔可夫过程的最新发展。我们首先基于可变分割介绍了广义框架，然后开发有效的方法来解决框架中出现的状态估算子问题。这些子问题的解决方案可以通过利用模型的马尔可夫结构来高效，就像在所谓的贝叶斯滤波和平滑方法中所做的那样。数值实验表明，我们的方法在计算成本和估计性能中的表现优于常规优化方法。
In this paper, we propose a class of efficient, accurate, and general methods for solving state-estimation problems with equality and inequality constraints. The methods are based on recent developments in variable splitting and partially observed Markov processes. We first present the generalized framework based on variable splitting, then develop efficient methods to solve the state-estimation subproblems arising in the framework. The solutions to these subproblems can be made efficient by leveraging the Markovian structure of the model as is classically done in so-called Bayesian filtering and smoothing methods. The numerical experiments demonstrate that our methods outperform conventional optimization methods in computation cost as well as the estimation performance.
