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Variable Splitting Methods for Constrained State Estimation in Partially Observed Markov Processes
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DOI：10.1109/LSP.2020.3010159.png)
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Abstract
En 中文
In this letter, 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.
Keywords:
Kalman filters
Optimization
Markov processes
State estimation
State-space methods
Minimization
Computational modeling
Constrained state estimation
inequality constraint
variable splitting
Kalman filtering and smoothing
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