A Novel Joint Multitarget Estimator for Multi-Bernoulli Models

In this paper, the joint multitarget (JoM) estimator proposed for the joint target detection and tracking (JoTT) filter is reformulated for the Gaussian mixture (GM) implementations of the multitarget multi-Bernoulli (MeMBer) filters. For this purpose, a mode-finding algorithm is employed to search for the most significant mode of a GM density. Thus, the maximum a posterior (MAP) estimates of Bernoulli targets are determined. In addition, the multi-Bernoulli versions of the two conflicting objective functions for the Pareto-optimal value of the unknown JoM estimation constant are derived. Simulations compare the performance of the proposed JoM estimator with that of the marginal multitarget (MaM) estimator in a multitarget tracking scenario, where the probability of target detection is a function of target states. The simulation results demonstrate that the proposed JoM estimator outperforms the MaM estimator under moderately low-observable conditions. This is because the incomplete cost function of the MaM estimator is not adequate to obtain accurate cardinality estimates of targets without considering how well targets are localized. Nevertheless, the proposed JoM estimator may suffer from track termination latency more than the MaM estimator due to the definition of its cost function.