Bayesian Extended Object Smoothing for the Random Matrix Model
Paper in proceedings, 2019

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.

Wishart

Gaussian

smoothing

random matrix

Extended object tracking

inverse Wishart

Author

Karl Granström

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Jakob Bramstång

Knightec AB

FUSION 2019 - 22nd International Conference on Information Fusion

9011388

22nd International Conference on Information Fusion, FUSION 2019
Ottawa, Canada,

Subject Categories

Other Medical Engineering

Probability Theory and Statistics

Control Engineering

More information

Latest update

10/8/2020