# Decentralized Poisson Multi-Bernoulli Filtering for Vehicle Tracking

## TL;DR

This paper introduces a decentralized Poisson multi-Bernoulli filter for multi-vehicle tracking using high-resolution sensors, combining independent Gaussian process extent models with efficient fusion techniques to improve tracking accuracy.

## Contribution

It presents a novel decentralized filtering approach that fuses independent vehicle estimates with Gaussian process models, enhancing multi-vehicle tracking capabilities.

## Key findings

- Numerical results show improved tracking performance.
- Efficient implementation with parametric state representation.
- Effective fusion of vehicle information enhances accuracy.

## Abstract

A decentralized Poisson multi-Bernoulli filter is proposed to track multiple vehicles using multiple high-resolution sensors. Independent filters estimate the vehicles' presence, state, and shape using a Gaussian process extent model; a decentralized filter is realized through fusion of the filters posterior densities. An efficient implementation is achieved by parametric state representation, utilization of single hypothesis tracks, and fusion of vehicle information based on a fusion mapping. Numerical results demonstrate the performance.