Bayesian Track-Before-Detect for closely spaced targets

Track-Before-Detect (TBD) is an effective approach to multi-target tracking problems with low signal-to-noise (SNR) ratio. In this paper we propose a novel Labeled Random Finite Set (RFS) solution to the multi-target TBD problem for a generic pixel based measurement model. In particular, we discuss the applicability of the Generalized Labeled Multi-Bernoulli (GLMB) distribution to the TBD problem for low SNR and closely spaced targets. In such case, the commonly used separable targets assumption does not hold and a more sophisticated algorithm is required. The proposed GLMB recursion is effective in the sense that it matches the cardinality distribution and Probability Hypothesis Density (PHD) function of the true joint posterior density. The approach is validated through simulation results in challenging scenarios.