Damped Posterior Linearization Filter

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2018-02-14
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
536-540
Series
IEEE Signal Processing Letters, Volume 25, issue 4
Abstract
In this letter, we propose an iterative Kalman type algorithm based on posterior linearization. The proposed algorithm uses a nested loop structure to optimize the mean of the estimate in the inner loop and update the covariance, which is a computationally more expensive operation, only in the outer loop. The optimization of the mean update is done using a damped algorithm to avoid divergence. Our simulations show that the proposed algorithm is more accurate than existing iterative Kalman filters.
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Keywords
Signal processing algorithms, Kalman filters, Noise measurement, Computational modeling, Cost function, Convergence, Bayesian state estimation, estimation, nonlinear
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Citation
Raitoharju, M, Svensson, L, Garcia Fernandez, A & Piche, R 2018, ' Damped Posterior Linearization Filter ', IEEE Signal Processing Letters, vol. 25, no. 4, pp. 536-540 . https://doi.org/10.1109/LSP.2018.2806304