A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements



Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455, Ralph, Jason ORCID: 0000-0002-4946-9948, Horridge, Paul and Maskell, Simon ORCID: 0000-0003-1917-2913
(2021) A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements. IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS, 57 (5). pp. 3539-3548.

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Abstract

This article proposes a Gaussian filtering method to approximate the single-target updates and normalizing constants for multitarget tracking with nonlinear, non-Gaussian measurements, and a state-dependent probability of detection. The Gaussian approximation is based on the posterior linearization technique, which seeks the optimal affine approximation of the nonlinearities in a mean square error sense. The normalizing constant is approximated using sigma-points based on the posterior. The proposed approach is implemented in a Poisson multi-Bernoulli mixture filter and compared against standard methods to approximate single-target posteriors and normalizing constants in two range-bearings tracking scenarios.

Item Type: Article
Uncontrolled Keywords: Gaussian approximation, Kalman filters, Density measurement, Standards, Current measurement, Area measurement, Target tracking, Multiple target tracking, nonlinear non-Gaussian measurements, state-dependent detection probability
Divisions: Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User: Symplectic Admin
Date Deposited: 11 Oct 2021 10:11
Last Modified: 15 Mar 2024 03:20
DOI: 10.1109/TAES.2021.3074200
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3140049

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