Abstract
In this paper we derive a particle flow particle filter implementation of the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) filter. The bootstrap particle filter δ-GLMB suffers from weight degeneracy for high-dimensional state systems or low measurement noise. In order to avoid weight degeneracy, we employ particle flow to produce a measurement-driven importance distribution that serves as a proposal in the δ-GLMB particle filter. Flow-induced proposals are developed for both types of targets encountered in the δ-GLMB filter, i.e., persistent and birth targets. Numerical simulations reflect the improved performance of the proposed filter with respect to classical bootstrap implementations.