Centralized multi-sensor multi-target tracking with labeled random finite sets
Baishen Wei, Brett Nener, Weifeng Liu, Liang Ma · 2016
This paper addresses the problem of multi-sensor multi-target tracking. The main contribution is an efficient implementation of the multi-sensor δ-Generalized labeled Multi-Bernoulli (δ-GLMB) update. To truncate the weighted sums of the multi-target exponentials, the ranked assignment algorithm is used in the update to determine the most important terms without computing all the terms. Simulation experiments via linear Gaussian mixture models confirm the effectiveness of the proposed algorithm.