An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models

Recent development of the multi-sensor generalised labelled multi-Bernoulli (MS-GLMB) tracking algorithm allows joint estimation of target trajectories adjunct to clutter rate and detection probability. Nevertheless, it requires prior knowledge of new birth target distribution which might not be ava...

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Main Authors: Nguyen, Tran Thien Dat, Do, C.T., Nguyen, Hoa Van
Format: Conference Paper
Published: 2022
Online Access:http://purl.org/au-research/grants/arc/LP200301507
http://hdl.handle.net/20.500.11937/96500
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author Nguyen, Tran Thien Dat
Do, C.T.
Nguyen, Hoa Van
author_facet Nguyen, Tran Thien Dat
Do, C.T.
Nguyen, Hoa Van
author_sort Nguyen, Tran Thien Dat
building Curtin Institutional Repository
collection Online Access
description Recent development of the multi-sensor generalised labelled multi-Bernoulli (MS-GLMB) tracking algorithm allows joint estimation of target trajectories adjunct to clutter rate and detection probability. Nevertheless, it requires prior knowledge of new birth target distribution which might not be available in certain tracking scenarios. Conversely, another algorithm has been proposed to handle unknown birth statistics using multi-sensor measurement and a Gibbs sampler, but not be able to estimate clutter rate and detection probability. In this paper, we propose a multi-sensor multi-target tracking algorithm to handle unknown clutter rate, detection profile, and statistics of new birth targets. Our algorithm assumes linear Gaussian property on the dynamic and measurement models for closed-form analytic computation. Experiment with a 3-D tracking scenario demonstrates the robustness of our algorithm.
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format Conference Paper
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T11:46:43Z
publishDate 2022
recordtype eprints
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spelling curtin-20.500.11937-965002025-01-09T06:25:59Z An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models Nguyen, Tran Thien Dat Do, C.T. Nguyen, Hoa Van Recent development of the multi-sensor generalised labelled multi-Bernoulli (MS-GLMB) tracking algorithm allows joint estimation of target trajectories adjunct to clutter rate and detection probability. Nevertheless, it requires prior knowledge of new birth target distribution which might not be available in certain tracking scenarios. Conversely, another algorithm has been proposed to handle unknown birth statistics using multi-sensor measurement and a Gibbs sampler, but not be able to estimate clutter rate and detection probability. In this paper, we propose a multi-sensor multi-target tracking algorithm to handle unknown clutter rate, detection profile, and statistics of new birth targets. Our algorithm assumes linear Gaussian property on the dynamic and measurement models for closed-form analytic computation. Experiment with a 3-D tracking scenario demonstrates the robustness of our algorithm. 2022 Conference Paper http://hdl.handle.net/20.500.11937/96500 10.1109/ICCAIS56082.2022.9990549 http://purl.org/au-research/grants/arc/LP200301507 fulltext
spellingShingle Nguyen, Tran Thien Dat
Do, C.T.
Nguyen, Hoa Van
An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
title An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
title_full An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
title_fullStr An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
title_full_unstemmed An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
title_short An Adaptive Multi-Sensor Generalised Labelled Multi-Bernoulli Filter for Linear Gaussian Models
title_sort adaptive multi-sensor generalised labelled multi-bernoulli filter for linear gaussian models
url http://purl.org/au-research/grants/arc/LP200301507
http://hdl.handle.net/20.500.11937/96500