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 proceedingConference 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 languageEnglish
Title of host publication2023 Sensor Signal Processing for Defence Conference (SSPD)
PublisherIEEE Xplore
Number of pages6
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
  • Stone Soup
  • Tracking

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