Abstract:

Aiming at the problem that the standard δ-generalized labeled multi-Bernoulli (δ-GLMB) filter requires a priori knowledge of target birth distributions, which leads to the reduction of estimation accuracy in a real world scenario, an adaptive target birth δ-GLMB filtering algorithm is proposed. Based on the δ-GLMB filter, the new algorithm approximates the existence probabilities and kinematic states of birth targets using measured data from the previous time, and provides parameterized representations of labeled Bernoulli random finite sets of new birth targets in the current time. Simulation results indicate that the proposed algorithm has a strong robustness, and a better performance in tracking accuracy and time consumption than the standard δ-GLMB filtering algorithm under the unknown priori knowledge of the birth targets complex scenario.

Key words: multi-target tracking, random finite sets, δ-generalized labeled multi-Bernoulli, adaptive target birth