# Bayesian Smoothing for the Extended Object Random Matrix Model

arXiv: https://arxiv.org/abs/1901.05301 · HTML: https://mlsanity.com/paper/1901.05301/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

## Built on by (most cited first, 1 of 1)

- [Extended Object Tracking Using Sets Of Trajectories with a PHD Filter](https://mlsanity.com/paper/2109.01019/) (2021)

## Abstract (arXiv)

The random matrix model is popular in extended object tracking, due to its relative simplicity and versatility. In this model, the extended object state consists of a kinematic vector for the position and motion parameters (velocity, etc), and an extent matrix. Two versions of the model can be found in literature, one where the state density is modelled by a conditional density, and one where the state density is modelled by a factorized density. In this paper, we present closed form Bayesian smoothing expression for both the conditional and the factorised model. In a simulation study, we compare the performance of different versions of the smoother.