Poisson multi-Bernoulli mixture filter with general target-generated measurements and arbitrary clutter
Journal article, 2023

This paper shows that the Poisson multi-Bernoulli mixture (PMBM) density is a multi-target conjugate prior for general target-generated measurement distributions and arbitrary clutter distributions. That is, for this multi-target measurement model and the standard multi-target dynamic model with Poisson birth model, the predicted and filtering densities are PMBMs. We derive the corresponding PMBM filtering recursion. Based on this result, we implement a PMBM filter for point-target measurement models and negative binomial clutter density in which data association hypotheses with high weights are chosen via Gibbs sampling. We also implement an extended target PMBM filter with clutter that is the union of Poisson-distributed clutter and a finite number of independent clutter sources. Simulation results show the benefits of the proposed filters to deal with non-standard clutter.

Sea measurements

Probabilistic logic


Poisson multi-Bernoulli mixtures

Gibbs sampling

Data models

Multi-target filtering


arbitrary clutter

Density measurement

Predictive models


Angel Garcia

University of Liverpool

Nebrija University

Yuxuan Xia

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

IEEE Transactions on Signal Processing

1053-587X (ISSN) 1941-0476 (eISSN)

Vol. 71 1895-1906

Subject Categories

Control Engineering

Signal Processing



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