Work

Damped Posterior Linearization Filter

Year: 2018

Type: article

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.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2018.2806304

Authors: Matti Raitoharju, Lennart Svensson, Angel Froilan Garcia-Fernandez, Robert Piche

Institutions: Tampere University of Technology, Aalto University, Chalmers University of Technology, University of Liverpool

Language: English

Topic: Target Tracking and Data Fusion in Sensor Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Academy of Finland

Award: G6648796183