Damped Posterior Linearization Filter

Matti Raitoharju, Lennart Svensson, Angel Garcia Fernandez, Robert Piche

Research output: Contribution to journalArticleScientificpeer-review

5 Citations (Scopus)
147 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)536-540
Number of pages5
JournalIEEE Signal Processing Letters
Issue number4
Publication statusPublished - 14 Feb 2018
MoE publication typeA1 Journal article-refereed


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

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