Constrained d-GLMB Filter for Multi-Target Track-Before-Detect using Radar Measurements

Multi-target Track-Before-Detect (TBD) algorithms are of great interest in many surveillance applications using Radar measurements. When low sensor resolution and/or low Signal-to-Noise Ratio (SNR) limit the tracking performance, exploiting additional information about the targets and/or the scenario becomes fundamental. In this paper, we consider a novel application of the d-Generalized Labeled Multi-Bernoulli (d-GLMB) filter for ground and/or maritime TBD problems where additional information is modeled using constraints on the target dynamics. Specifically, state constraints are used to model the additional information about the surveillance area, and a generalized likelihood function is derived to enforce the constraints in the update step of the d-GLMB filter. Simulations results for a scenario with low resolution and low SNR verify the applicability of the proposed approach.