Multi-Scan Multi-Sensor Multi-Object State Estimation

If computational tractability were not an issue, multi-object estimation should integrate all measurements from multiple sensors across scans. In this article, we propose efficient numerical solution to the multi-scan multi-sensor problem by computing (labeled) posterior density. Minimizing $L_{1}$ -norm error exact density requires solving large-scale multi-dimensional assignment problems that are NP-hard. An algorithm is developed based on Gibbs sampling, together with convergence analysis. The resulting can be applied either offline in one batch or recursively. efficacy of demonstrated using experiments a simulated dataset.