  In recent years, Bayes filter methods in the labeled random finite set
formulation have become increasingly powerful in the multi-target tracking
domain. One of the latest outcomes is the Generalized Labeled Multi-Bernoulli
(GLMB) filter which allows for stable cardinality and target state estimation
as well as target identification in a unified framework. In contrast to the
initial context of the GLMB filter, this paper makes use of it in the
Track-Before-Detect (TBD) scheme and thus, avoids information loss due to
thresholding and other data preprocessing steps. This paper provides a TBD GLMB
filter design under the separable likelihood assumption that can be applied to
real world scenarios and data in the automotive radar context. Its
applicability to real sensor data is demonstrated in an exemplary scenario. To
the best of the authors' knowledge, the GLMB filter is applied to real radar
data in a TBD framework for the first time.
