Adaptive Kernel Kalman Filter based Belief Propagation Algorithm for Maneuvering Multi-target Tracking

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

doi:10.1109/LSP.2022.3184534

Adaptive Kernel Kalman Filter based Belief Propagation Algorithm for Maneuvering Multi-target Tracking

Mengwei Sun^*, Michael E. Davies, Ian Proudler, James R. Hopgood (Lead Author)

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract / Description of output

This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.

Original language	English
Pages (from-to)	1452-1456
Journal	IEEE Signal Processing Letters
Volume	29
Early online date	20 Jun 2022
DOIs	https://doi.org/10.1109/LSP.2022.3184534
Publication status	E-pub ahead of print - 20 Jun 2022

Keywords / Materials (for Non-textual outputs)

Adaptive kernel Kalman filter
belief propagation
data association
multi-target tracking
Target tracking
Signal processing algorithms
Probability density function
Kalman filters
Proposals
Kernel
Covariance matrices

Access to Document

10.1109/LSP.2022.3184534

Author ManuscriptAccepted author manuscript, 806 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

5 Conference contribution
1 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
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
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

Cite this

@article{8dc2da5a82ad41fa8f0dfc4b6623123a,

title = "Adaptive Kernel Kalman Filter based Belief Propagation Algorithm for Maneuvering Multi-target Tracking",

abstract = "This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.",

keywords = "Adaptive kernel Kalman filter, belief propagation, data association, multi-target tracking, Target tracking, Signal processing algorithms, Probability density function, Kalman filters, Proposals, Kernel, Covariance matrices",

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

note = "Publisher Copyright: IEEE",

year = "2022",

month = jun,

day = "20",

doi = "10.1109/LSP.2022.3184534",

language = "English",

volume = "29",

pages = "1452--1456",

journal = "IEEE Signal Processing Letters",

issn = "1070-9908",

publisher = "Institute of Electrical and Electronics Engineers",

}

TY - JOUR

T1 - Adaptive Kernel Kalman Filter based Belief Propagation Algorithm for Maneuvering Multi-target Tracking

AU - Sun, Mengwei

AU - Davies, Michael E.

AU - Proudler, Ian

AU - Hopgood, James R.

N1 - Publisher Copyright: IEEE

PY - 2022/6/20

Y1 - 2022/6/20

N2 - This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.

AB - This letter incorporates the adaptive kernel Kalman filter (AKKF) into the belief propagation (BP) algorithm for Multi-target tracking (MTT) in single-sensor systems. The algorithm is capable of tracking an unknown and time-varying number of targets, in the presence of false alarms, clutter and measurement-to-target association uncertainty. Experiment results reveal that the proposed method has a favourable tracking performance using the generalized optimal sub-patten assignment (GOSAP) metrics at substantially less computation cost than the particle filter (PF) based Multi-target tracking (MTT) BP algorithm.

KW - Adaptive kernel Kalman filter

KW - belief propagation

KW - data association

KW - multi-target tracking

KW - Target tracking

KW - Signal processing algorithms

KW - Probability density function

KW - Kalman filters

KW - Proposals

KW - Kernel

KW - Covariance matrices

U2 - 10.1109/LSP.2022.3184534

DO - 10.1109/LSP.2022.3184534

M3 - Article

SN - 1070-9908

VL - 29

SP - 1452

EP - 1456

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

ER -

Adaptive Kernel Kalman Filter based Belief Propagation Algorithm for Maneuvering Multi-target Tracking

Abstract / Description of output

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

Implementation of Adaptive Kernel Kalman Filter in Stone Soup

Cite this