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

A Track-Before-Detect Approach to Multi-Target Tracking on Automotive Radar Sensor Data