Implementation of Adaptive Kernel Kalman Filter in Stone Soup

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

doi:10.1109/SSPD57945.2023.10256739

Implementation of Adaptive Kernel Kalman Filter in Stone Soup

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

Original language	English
Title of host publication	2023 Sensor Signal Processing for Defence Conference (SSPD)
Publisher	Institute of Electrical and Electronics Engineers
Number of pages	6
DOIs	https://doi.org/10.1109/SSPD57945.2023.10256739
Publication status	E-pub ahead of print - 22 Sept 2023
Event	2023 Sensor Signal Processing for Defence Conference - The Royal College of Physicians of Edinburgh, Edinburgh, United Kingdom Duration: 12 Sept 2023 → 13 Sept 2023 Conference number: 12 https://www.sspd.eng.ed.ac.uk/

Conference

Conference	2023 Sensor Signal Processing for Defence Conference
Abbreviated title	SSPD 2023
Country/Territory	United Kingdom
City	Edinburgh
Period	12/09/23 → 13/09/23
Internet address	https://www.sspd.eng.ed.ac.uk/

Keywords / Materials (for Non-textual outputs)

Adaptive kernel Kalman filter
Stone Soup
Tracking

Access to Document

10.1109/SSPD57945.2023.10256739

AKKF_Stone_Soup_ImplementationAccepted author manuscript, 281 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

Implementation of AKKF-based Multi-Sensor Fusion Methods in Stone Soup
Wright, J., Sun, M., Davies, M. E., Proudler, I. & Hopgood, J. R., 1 May 2024, (Accepted/In press) 2024 27th International Conference on Information Fusion. Institute of Electrical and Electronics Engineers, 6 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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
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{be0c27d9ac2e41538a810ff514f5052a,

title = "Implementation of Adaptive Kernel Kalman Filter in Stone Soup",

abstract = "The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.",

keywords = "Adaptive kernel Kalman filter, Stone Soup, Tracking",

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

note = "Funding Information: This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) Grant number EP/S000631/1; and the MOD University Defence Research Collaboration (UDRC) in Signal Processing. Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 Sensor Signal Processing for Defence Conference , SSPD 2023 ; Conference date: 12-09-2023 Through 13-09-2023",

year = "2023",

month = sep,

day = "22",

doi = "10.1109/SSPD57945.2023.10256739",

language = "English",

booktitle = "2023 Sensor Signal Processing for Defence Conference (SSPD)",

publisher = "Institute of Electrical and Electronics Engineers",

url = "https://www.sspd.eng.ed.ac.uk/",

}

Wright, J, Hopgood, JR , Davies, ME, Proudler, I & Sun, M 2023, Implementation of Adaptive Kernel Kalman Filter in Stone Soup. in 2023 Sensor Signal Processing for Defence Conference (SSPD). Institute of Electrical and Electronics Engineers, 2023 Sensor Signal Processing for Defence Conference , Edinburgh, United Kingdom, 12/09/23. https://doi.org/10.1109/SSPD57945.2023.10256739

TY - GEN

T1 - Implementation of Adaptive Kernel Kalman Filter in Stone Soup

AU - Wright, James

AU - Hopgood, James R.

AU - Davies, Michael E.

AU - Proudler, Ian

AU - Sun, Mengwei

N1 - Conference code: 12

PY - 2023/9/22

Y1 - 2023/9/22

N2 - The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

AB - The recently proposed adaptive kernel Kalman filter (AKKF) is an efficient method for highly nonlinear and high-dimensional tracking or estimation problems. Compared to other nonlinear Kalman filters (KFs), the AKKF has significantly improved performance, reducing computational complexity and avoiding resampling. It has been applied in various tracking scenarios, such as multi-sensor fusion and multi-target tracking. By using existing Stone Soup components, along with newly established kernel-based prediction and update modules, we demonstrate that the AKKF can work in the Stone Soup platform by being applied to a bearing-only tracking (BOT) problem. We hope that the AKKF will enable more applications for tracking and estimation problems, and the development of a whole class of derived algorithms in sensor fusion systems.

KW - Adaptive kernel Kalman filter

KW - Stone Soup

KW - Tracking

U2 - 10.1109/SSPD57945.2023.10256739

DO - 10.1109/SSPD57945.2023.10256739

M3 - Conference contribution

BT - 2023 Sensor Signal Processing for Defence Conference (SSPD)

PB - Institute of Electrical and Electronics Engineers

T2 - 2023 Sensor Signal Processing for Defence Conference

Y2 - 12 September 2023 through 13 September 2023

ER -

Implementation of Adaptive Kernel Kalman Filter in Stone Soup

Abstract / Description of output

Conference

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Projects

Signal Processing in the Information Age

Research output

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

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

Adaptive Kernel Kalman Filter

Cite this