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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publication2023 Sensor Signal Processing for Defence Conference (SSPD)
PublisherIEEE
Number of pages5
DOIs
Publication statusE-pub ahead of print - 22 Sept 2023
Event2023 Sensor Signal Processing for Defence Conference - The Royal College of Physicians of Edinburgh, Edinburgh, United Kingdom
Duration: 12 Sept 202313 Sept 2023
Conference number: 12
https://www.sspd.eng.ed.ac.uk/

Conference

Conference2023 Sensor Signal Processing for Defence Conference
Abbreviated titleSSPD 2023
Country/TerritoryUnited Kingdom
CityEdinburgh
Period12/09/2313/09/23
Internet address

Keywords / Materials (for Non-textual outputs)

  • Adaptive kernel Kalman filter
  • magnetic anomaly detection
  • metallic target tracking

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