Tampere University of Technology

TUTCRIS Research Portal

Damped Posterior Linearization Filter

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
JournalIEEE Signal Processing Letters
Volume25
Issue number4
Early online date13 Feb 2018
DOIs
Publication statusPublished - 2018
Publication typeA1 Journal article-refereed

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.

Keywords

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

Publication forum classification

Downloads statistics

No data available