Backward Simulation for Sets of Trajectories
arXiv:2008.02051
Abstract
This paper presents a solution for recovering full trajectory information, via the calculation of the posterior of the set of trajectories, from a sequence of multitarget (unlabelled) filtering densities and the multitarget dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multitarget filters that do not explicitly estimate trajectories. In this paper, we first derive a general multitrajectory forward-backward smoothing equation based on sets of trajectories and the random finite set framework. Then we show how to sample sets of trajectories using backward simulation when the multitarget filtering densities are multi-Bernoulli processes. The proposed approach is demonstrated in a simulation study.
arXiv commentPublished in 23rd International Conference on Information Fusion. This arXiv version contains more detailed derivations
References in corpus (7)
- Marginal multi-Bernoulli filters: RFS derivation of MHT, JIPDA and association-based MeMBer
- Poisson multi-Bernoulli mixture filter: direct derivation and implementation
- Multiple target tracking based on sets of trajectories
- An efficient, variational approximation of the best fitting multi-Bernoulli filter
- A metric on the space of finite sets of trajectories for evaluation of multi-target tracking algorithms
- Poisson Multi-Bernoulli Mapping Using Gibbs Sampling
- Hybrid Poisson and multi-Bernoulli filters