# Distributed Poisson multi-Bernoulli filtering via generalised covariance intersection

arXiv: https://arxiv.org/abs/2506.18397 · HTML: https://mlsanity.com/paper/2506.18397/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 19 of 19)

- [Generalized optimal sub-pattern assignment metric](https://mlsanity.com/paper/1601.05585/) (2016)
- [Poisson multi-Bernoulli mixture filter: direct derivation and implementation](https://mlsanity.com/paper/1703.04264/) (2017)
- [Collaborative Multi-Object Tracking with Conformal Uncertainty Propagation](https://mlsanity.com/paper/2303.14346/) (2023)
- [Trajectory Poisson multi-Bernoulli filters](https://mlsanity.com/paper/2003.12767/) (2020)
- [Poisson multi-Bernoulli conjugate prior for multiple extended object filtering](https://mlsanity.com/paper/1605.06311/) (2016)
- [Gaussian implementation of the multi-Bernoulli mixture filter](https://mlsanity.com/paper/1908.08819/) (2019)
- [Robust Distributed Fusion with Labeled Random Finite Sets](https://mlsanity.com/paper/1710.00501/) (2017)
- [5G mmWave Cooperative Positioning and Mapping using Multi-Model PHD Filter and Map Fusion](https://mlsanity.com/paper/1908.09806/) (2019)
- [An efficient, variational approximation of the best fitting multi-Bernoulli filter](https://mlsanity.com/paper/1403.5022/) (2014)
- [Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking](https://mlsanity.com/paper/1901.04518/) (2019)
- [A Poisson multi-Bernoulli mixture filter for coexisting point and extended targets](https://mlsanity.com/paper/2011.04464/) (2020)
- [A New Architecture for Neural Enhanced Multiobject Tracking](https://mlsanity.com/paper/2410.06294/) (2024)
- [Distributed Fusion with Multi-Bernoulli Filter based on Generalized Covariance Intersection](https://mlsanity.com/paper/1603.08340/) (2016)
- [Partial Consensus and Conservative Fusion of Gaussian Mixtures for Distributed PHD Fusion](https://mlsanity.com/paper/1711.10783/) (2017)
- [Multiobject fusion with minimum information loss](https://mlsanity.com/paper/1903.04239/) (2019)
- [Fusion of labeled RFS densities with minimum information loss](https://mlsanity.com/paper/1911.01083/) (2019)
- [Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter](https://mlsanity.com/paper/2210.12983/) (2022)
- [Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering](https://mlsanity.com/paper/1801.01353/) (2018)
- [Multiple Object Trajectory Estimation Using Backward Simulation](https://mlsanity.com/paper/2206.08112/) (2022)

## Abstract (arXiv)

This paper presents the distributed Poisson multi-Bernoulli (PMB) filter based on the generalised covariance intersection (GCI) fusion rule for distributed multi-object filtering. Since the exact GCI fusion of two PMB densities is intractable, we derive a principled approximation. Specifically, we approximate the power of a PMB density as an unnormalised PMB density, which corresponds to an upper bound of the PMB density. Then, the GCI fusion rule corresponds to the normalised product of two unnormalised PMB densities. We show that the result is a Poisson multi-Bernoulli mixture (PMBM), which can be expressed in closed form. Future prediction and update steps in each filter preserve the PMBM form, which can be projected back to a PMB density before the next fusion step. Experimental results show the benefits of this approach compared to other distributed multi-object filters.