Continuous-Discrete Multiple Target Tracking With Out-of-Sequence Measurements

This paper derives the optimal Bayesian processing of an out-of-sequence (OOS) set measurements in continuous-time for multiple target tracking. We consider a multi-target system modelled continuous time that is discretised at steps when we receive measurements, which are distributed according to standard point model. All information about this sampled provided by posterior density on all trajectories. can be computed via continuous-discrete trajectory Poisson multi-Bernoulli mixture (TPMBM) filter. When OOS measurement, performs retrodiction step adds measurement stamp followed update step. After update, remains TPMBM form. also provide computationally lighter alternative based The effectiveness two approaches handle evaluated simulations.