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Consensus Iterated Posterior Linearization Filter for Distributed State Estimation
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DOI：10.1109/LSP.2025.3526092.png)
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摘要
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This paper presents the consensus iterated posterior linearisation filter (IPLF) for distributed state estimation. The consensus IPLF algorithm is based on a measurement model described by its conditional mean and covariance given the state, and performs iterated statistical linear regressions of the measurements with respect to the current approximation of the posterior to improve estimation performance. Three variants of the algorithm are presented based on the type of consensus that is used: consensus on information, consensus on measurements, and hybrid consensus on measurements and information. Simulation results show the benefits of the proposed algorithm in distributed state estimation.
Keyword:
Covariance matrices
Approximation algorithms
Vectors
Kalman filters
Signal processing algorithms
Sensors
Consensus algorithm
State estimation
Current measurement
Noise measurement
Consensus
distributed state estimation
iterated posterior linearisation
nonlinear filtering
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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