Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup

James Wright*, Mengwei Sun, Michael E. Davies, Ian Proudler, James R. Hopgood

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper explores the increasing demand for accurate and resilient multi-sensor fusion techniques, particularly within 3D tracking systems enhanced by drone technology. Employing the adaptive kernel Kalman filter (AKKF) methodology within the Stone Soup framework, our research seeks to develop robust fusion approaches capable of seamlessly amalgamating data from a multi-sensor arrangement with fixed ground sensors and dynamic sensors mounted on drones. By capitalising on the adaptive nature of the AKKF, we aim to refine the precision and dependability of 3D object tracking in intricate scenarios. Through comprehensive empirical evaluations, we illustrate the effectiveness of our proposed AKKF-based fusion strategies in enhancing tracking performance within the Stone Soup framework, thus contributing to the advancement of multi-sensor fusion methodologies within this framework.
Original languageEnglish
Title of host publication2024 27th International Conference on Information Fusion
PublisherIEEE Xplore
Number of pages6
Publication statusAccepted/In press - 1 May 2024
EventThe 27th International Conference on Information Fusion - Venice, Italy
Duration: 7 Jul 202411 Jul 2024
https://fusion2024.org/

Conference

ConferenceThe 27th International Conference on Information Fusion
Abbreviated titleFUSION 2024
Country/TerritoryItaly
CityVenice
Period7/07/2411/07/24
Internet address

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