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Damped Posterior Linearization Filter

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Original languageEnglish
JournalIEEE Signal Processing Letters
Issue number4
Early online date13 Feb 2018
Publication statusPublished - 2018
Publication typeA1 Journal article-refereed


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.


  • Bayesian state estimation, Computational modeling, Convergence, Cost function, estimation, Kalman filters, Noise measurement, nonlinear, Signal processing algorithms

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