Poisson multi-Bernoulli conjugate prior for multiple extended object filtering
This work addresses tracking challenges in sensor systems like Lidar for applications such as autonomous vehicles, but it is incremental as it builds on prior conjugate prior frameworks with approximations for tractability.
The paper tackled the problem of multiple extended object filtering by introducing a Poisson multi-Bernoulli mixture (PMBM) conjugate prior, which models undetected and detected targets separately, and showed that the filter performs well in simulations compared to existing methods like d-GLMB and LMB filters.
This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object filtering. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter performs well in comparison to the extended target d-GLMB and LMB filters. An experiment with Lidar data illustrates the benefit of tracking both detected and undetected targets.