Implementation of adaptive kernel Kalman filter in stone soup

James S. Wright; James R. Hopgood; Mike E. Davies; Ian K. Proudler; Mengwei Sun

doi:10.1109/sspd57945.2023.10256739

Implementation of adaptive kernel Kalman filter in stone soup

James S. Wright, James R. Hopgood, Mike E. Davies, Ian K. Proudler, Mengwei Sun

Electronic And Electrical Engineering

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

3 Citations (Scopus)

103 Downloads (Pure)

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.

Original language	English
Title of host publication	2023 Sensor Signal Processing for Defence Conference (SSPD)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	9798350337327
DOIs	https://doi.org/10.1109/sspd57945.2023.10256739
Publication status	Published - 22 Sept 2023

Keywords

adaptive kernel Kalman filter
tracking
stone soup

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10.1109/sspd57945.2023.10256739

Wright-etal-SSPD-2023-Implementation-of-adaptive-kernel-Kalman-filterAccepted author manuscript, 602 KBLicence: Strath_1

Cite this

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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.",

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

note = "{\textcopyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",

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AU - Hopgood, James R.

AU - Davies, Mike E.

AU - Proudler, Ian K.

AU - Sun, Mengwei

N1 - © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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 - tracking

KW - stone soup

U2 - 10.1109/sspd57945.2023.10256739

DO - 10.1109/sspd57945.2023.10256739

M3 - Conference contribution book

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EP - 5

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

PB - IEEE

CY - Piscataway, NJ

ER -

Implementation of adaptive kernel Kalman filter in stone soup

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Keywords

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