Damped Posterior Linearization Filter
Artikel i vetenskaplig tidskrift, 2018

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.

kalman filters

nonlinear

Bayesian state estimation

estimation

Författare

Matti Raitoharju

Tammerfors tekniska universitet

Aalto-Yliopisto

Lennart Svensson

Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik, Signalbehandling

Angel Froilan Garcia-Fernandez

University of Liverpool

Robert Piche

Tammerfors tekniska universitet

IEEE Signal Processing Letters

1070-9908 (ISSN)

Vol. 25 536-540

Ämneskategorier

Sannolikhetsteori och statistik

Reglerteknik

Signalbehandling

DOI

10.1109/LSP.2018.2806304