# Marginalized Particle Flow Filter

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## Abstract

As an alternative to the Kalman filter and the particle filter, the particle flow filter has recently attracted interest for solving the curse of dimensionality of the particle filter. Compared with the particle filter, the particle flow filter can obtain a better performance in high-dimensional state spaces with fewer samples. However, for some unobservable state dimensions, the flow operation wastes computational resources. In this paper, we propose a marginalized particle flow filter to handle the unobservable sub-state estimation. In contrast to the standard particle flow filter, we only migrate those observable dimensions of each particle according to homotopy theory and estimate the unobservable dimensions using the Kalman filter. The proposed algorithm can enhance the estimation quality of the unobservable state space and reduce the runtime of the particle flow filter. We evaluate the performance of the proposed algorithm through a multi-target tracking simulation.

## Keywords

Nonlinear filter Particle filter Particle flow High-dimensional filtering## Notes

### Acknowledgements

We are grateful to the referees for their clarifying suggestions, which have improved the presentation of this material, and in articular to Jeremie Houssineau for his comments. This study was supported by the National Natural Science Foundation of China (NSFC, Grant No. 61305013).

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