Gaussian implementation of the multi-Bernoulli mixture filter
This work addresses multi-target tracking in radar applications, but it is incremental as it adapts an existing filter to a specific model without introducing new paradigms.
The paper tackled the problem of multi-target tracking under linear/Gaussian models by presenting a Gaussian implementation of the multi-Bernoulli mixture filter, resulting in closed-form Gaussian expressions for single-target densities and using Murty's algorithm for hypothesis selection, with performance evaluated via numerical simulations.
This paper presents the Gaussian implementation of the multi-Bernoulli mixture (MBM) filter. The MBM filter provides the filtering (multi-target) density for the standard dynamic and radar measurement models when the birth model is multi-Bernoulli or multi-Bernoulli mixture. Under linear/Gaussian models, the single target densities of the MBM mixture admit Gaussian closed-form expressions. Murty's algorithm is used to select the global hypotheses with highest weights. The MBM filter is compared with other algorithms in the literature via numerical simulations.