Stone Soup: ADS-B-based Multi-Target Tracking with Stochastic Integration Filter

TL;DR

This paper evaluates multi-target tracking using the Stone Soup framework with ADS-B data, comparing different local estimators across simulated datasets and providing source code for reproducibility.

Contribution

It introduces an evaluation of multi-target tracking scenarios with Stone Soup using real ADS-B datasets and offers a comprehensive analysis of estimator performance.

Findings

Different local estimators impact tracking accuracy

Evaluation metrics help compare tracking scenarios

Source code facilitates reproducibility and further research

Abstract

This paper focuses on the multi-target tracking using the Stone Soup framework. In particular, we aim at evaluation of two multi-target tracking scenarios based on the simulated class-B dataset and ADS-B class-A dataset provided by OpenSky Network. The scenarios are evaluated w.r.t. selection of a local state estimator using a range of the Stone Soup metrics. Source code with scenario definitions and Stone Soup set-up are provided along with the paper.