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

Mengwei Sun; Richard Hodgskin-Brown; Michael E. Davies; Ian Proudler; James R. Hopgood

doi:10.1109/SSPD57945.2023.10256968

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

Mengwei Sun^*, Richard Hodgskin-Brown, Michael E. Davies, Ian Proudler, James R. Hopgood

^*Corresponding author for this work

School of Engineering

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

Abstract / Description of output

This paper proposes the use of the adaptive kernel Kalman filter (AKKF) to track metallic targets using magnetic anomaly detection (MAD). The proposed AKKF-based approach enables accurate tracking of moving metallic targets using magnetometer sensors, even in the presence of dynamic and unknown magnetic moments. The experimental results demonstrate that the proposed method exhibits favourable tracking and estimation performance with reduced computational complexity compared with the bootstrap particle filter (PF). For example, in magnetic moment strength estimation, the relative root mean square error (RRMSE) of the proposed algorithm using 50 particles can approach 2.5% with a computation time of 0.18 seconds, whereas the RRMSE of the PF using 2000 particles is 4.5% with a computation time of 1.4 seconds. This study highlights the potential of AKKF in MAD for metallic target tracking using magnetometer sensors.

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	5
DOIs	https://doi.org/10.1109/SSPD57945.2023.10256968
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
magnetic anomaly detection
metallic target tracking

Access to Document

10.1109/SSPD57945.2023.10256968

ED_Metrasens_SSPD_conference_paper__V3Accepted author manuscript, 0.99 MBLicence: 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., 11 Oct 2024, (E-pub ahead of print) 2024 27th International Conference on Information Fusion (FUSION). Institute of Electrical and Electronics Engineers, 6 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{211901aca7db49ab8b184f1f8b725918,

title = "Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection-based Metallic Target Tracking",

abstract = "This paper proposes the use of the adaptive kernel Kalman filter (AKKF) to track metallic targets using magnetic anomaly detection (MAD). The proposed AKKF-based approach enables accurate tracking of moving metallic targets using magnetometer sensors, even in the presence of dynamic and unknown magnetic moments. The experimental results demonstrate that the proposed method exhibits favourable tracking and estimation performance with reduced computational complexity compared with the bootstrap particle filter (PF). For example, in magnetic moment strength estimation, the relative root mean square error (RRMSE) of the proposed algorithm using 50 particles can approach 2.5% with a computation time of 0.18 seconds, whereas the RRMSE of the PF using 2000 particles is 4.5% with a computation time of 1.4 seconds. This study highlights the potential of AKKF in MAD for metallic target tracking using magnetometer sensors.",

keywords = "Adaptive kernel Kalman filter, magnetic anomaly detection, metallic target tracking",

author = "Mengwei Sun and Richard Hodgskin-Brown and Davies, {Michael E.} and Ian Proudler and Hopgood, {James R.}",

note = "Funding Information: Richard Hodgskin-Brown is with the Electromagnetic Sensing Group, Department of Electronics & Electrical Engineering, University of Manchester, Manchester, M13 9PL, UK. E-mail: [email protected] 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; EPSRC Impact Acceleration Accounts (IAA) Project number PV022. 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/SSPD57945.2023.10256968",

language = "English",

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

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

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

}

Sun, M, Hodgskin-Brown, R, Davies, ME, Proudler, I & Hopgood, JR 2023, Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection-based Metallic Target Tracking. 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/SSPD57945.2023.10256968

TY - GEN

T1 - Adaptive Kernel Kalman Filter for Magnetic Anomaly Detection-based Metallic Target Tracking

AU - Sun, Mengwei

AU - Hodgskin-Brown, Richard

AU - Davies, Michael E.

AU - Proudler, Ian

AU - Hopgood, James R.

N1 - Conference code: 12

PY - 2023/9/22

Y1 - 2023/9/22

N2 - This paper proposes the use of the adaptive kernel Kalman filter (AKKF) to track metallic targets using magnetic anomaly detection (MAD). The proposed AKKF-based approach enables accurate tracking of moving metallic targets using magnetometer sensors, even in the presence of dynamic and unknown magnetic moments. The experimental results demonstrate that the proposed method exhibits favourable tracking and estimation performance with reduced computational complexity compared with the bootstrap particle filter (PF). For example, in magnetic moment strength estimation, the relative root mean square error (RRMSE) of the proposed algorithm using 50 particles can approach 2.5% with a computation time of 0.18 seconds, whereas the RRMSE of the PF using 2000 particles is 4.5% with a computation time of 1.4 seconds. This study highlights the potential of AKKF in MAD for metallic target tracking using magnetometer sensors.

AB - This paper proposes the use of the adaptive kernel Kalman filter (AKKF) to track metallic targets using magnetic anomaly detection (MAD). The proposed AKKF-based approach enables accurate tracking of moving metallic targets using magnetometer sensors, even in the presence of dynamic and unknown magnetic moments. The experimental results demonstrate that the proposed method exhibits favourable tracking and estimation performance with reduced computational complexity compared with the bootstrap particle filter (PF). For example, in magnetic moment strength estimation, the relative root mean square error (RRMSE) of the proposed algorithm using 50 particles can approach 2.5% with a computation time of 0.18 seconds, whereas the RRMSE of the PF using 2000 particles is 4.5% with a computation time of 1.4 seconds. This study highlights the potential of AKKF in MAD for metallic target tracking using magnetometer sensors.

KW - Adaptive kernel Kalman filter

KW - magnetic anomaly detection

KW - metallic target tracking

U2 - 10.1109/SSPD57945.2023.10256968

DO - 10.1109/SSPD57945.2023.10256968

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 -

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

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

Implementation of Adaptive Kernel Kalman Filter in Stone Soup

Adaptive Kernel Kalman Filter

Cite this