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

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

doi:10.23919/FUSION59988.2024.10706335

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 proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	2024 27th International Conference on Information Fusion (FUSION)
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	6
ISBN (Electronic)	978-1-7377497-6-9
ISBN (Print)	979-8-3503-7142-0
DOIs	https://doi.org/10.23919/FUSION59988.2024.10706335
Publication status	E-pub ahead of print - 11 Oct 2024
Event	The 27th International Conference on Information Fusion - Venice, Italy Duration: 7 Jul 2024 → 11 Jul 2024 https://fusion2024.org/

Conference

Conference	The 27th International Conference on Information Fusion
Abbreviated title	FUSION 2024
Country/Territory	Italy
City	Venice
Period	7/07/24 → 11/07/24
Internet address	https://fusion2024.org/

Keywords / Materials (for Non-textual outputs)

3D Tracking
Adaptive kernel Kalman filter
sensor fusion, Stone Soup

Access to Document

10.23919/FUSION59988.2024.10706335

Fusion_2024_Implementation_of_the_AKKF_based_fusion_methods_in_Stone_Soup-SUBMITTEDAccepted author manuscript, 642 KBLicence: Creative Commons: Attribution (CC-BY)

1 Finished

Signal Processing in the Information Age
Davies, M., Hopgood, J., Hospedales, T., Mulgrew, B., Thompson, J., Tsaftaris, S. & Yaghoobi Vaighan, M.
EPSRC
1/07/18 → 31/03/24
Project: Research

4 Conference contribution
2 Article

Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection-based Metallic Target Tracking
Sun, M., Hodgskin-Brown, R., Davies, M. E., Proudler, I. & Hopgood, J. R., 22 Sept 2023, (E-pub ahead of print) 2023 Sensor Signal Processing for Defence Conference (SSPD) . Institute of Electrical and Electronics Engineers, 5 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access
File
Implementation of Adaptive Kernel Kalman Filter in Stone Soup
Wright, J., Hopgood, J. R., Davies, M. E., Proudler, I. & Sun, M., 22 Sept 2023, (E-pub ahead of print) 2023 Sensor Signal Processing for Defence Conference (SSPD). Institute of Electrical and Electronics Engineers, 6 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access
File
Adaptive Kernel Kalman Filter
Sun, M., Davies, M. E., Proudler, I. & Hopgood, J. R., 8 Mar 2023, (E-pub ahead of print) In: IEEE Transactions on Signal Processing. 71, p. 713-726
Research output: Contribution to journal › Article › peer-review

Open Access
File

Cite this

@inproceedings{c5596ee4db59458cb9277b1453b41bc4,

title = "Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup",

abstract = "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.",

keywords = "3D Tracking, Adaptive kernel Kalman filter, sensor fusion, Stone Soup",

author = "James Wright and Mengwei Sun and Davies, {Michael E.} and Ian Proudler and Hopgood, {James R.}",

year = "2024",

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doi = "10.23919/FUSION59988.2024.10706335",

language = "English",

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booktitle = "2024 27th International Conference on Information Fusion (FUSION)",

publisher = "Institute of Electrical and Electronics Engineers",

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note = "The 27th International Conference on Information Fusion, FUSION 2024 ; Conference date: 07-07-2024 Through 11-07-2024",

url = "https://fusion2024.org/",

}

Wright, J, Sun, M, Davies, ME, Proudler, I & Hopgood, JR 2024, Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup. in 2024 27th International Conference on Information Fusion (FUSION). Institute of Electrical and Electronics Engineers, The 27th International Conference on Information Fusion, Venice, Italy, 7/07/24. https://doi.org/10.23919/FUSION59988.2024.10706335

TY - GEN

T1 - Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup

AU - Wright, James

AU - Sun, Mengwei

AU - Davies, Michael E.

AU - Proudler, Ian

AU - Hopgood, James R.

PY - 2024/10/11

Y1 - 2024/10/11

N2 - 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.

AB - 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.

KW - 3D Tracking

KW - Adaptive kernel Kalman filter

KW - sensor fusion, Stone Soup

U2 - 10.23919/FUSION59988.2024.10706335

DO - 10.23919/FUSION59988.2024.10706335

M3 - Conference contribution

SN - 979-8-3503-7142-0

BT - 2024 27th International Conference on Information Fusion (FUSION)

PB - Institute of Electrical and Electronics Engineers

T2 - The 27th International Conference on Information Fusion

Y2 - 7 July 2024 through 11 July 2024

ER -

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

Abstract

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Projects

Signal Processing in the Information Age

Research output

Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection-based Metallic Target Tracking

Implementation of Adaptive Kernel Kalman Filter in Stone Soup

Adaptive Kernel Kalman Filter

Cite this