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.
A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements
IEEE Transactions on Aerospace and Electronic Systems ; 57 , 5 ; 3539-3548
01.10.2021
576850 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch