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Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation

This paper provides a solution for multi-target tracking with unknown detection probability. For the standard Poisson Multi-Bernoulli Mixture (PMBM) filter, the detection probability is generally considered a priori. However, affected by sensors, the features used for detection, and other environmen...

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Detalles Bibliográficos
Autores principales: Wang, Yi, Rao, Peng, Chen, Xin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143192/
https://www.ncbi.nlm.nih.gov/pubmed/35632139
http://dx.doi.org/10.3390/s22103730
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author Wang, Yi
Rao, Peng
Chen, Xin
author_facet Wang, Yi
Rao, Peng
Chen, Xin
author_sort Wang, Yi
collection PubMed
description This paper provides a solution for multi-target tracking with unknown detection probability. For the standard Poisson Multi-Bernoulli Mixture (PMBM) filter, the detection probability is generally considered a priori. However, affected by sensors, the features used for detection, and other environmental factors, the detection probability is time-varying and unknown in most multi-target tracking scenarios. Therefore, the standard PMBM filter is not feasible in practical scenarios. In order to overcome these practical restrictions, we improve the PMBM filter with unknown detection probability using the feature used for detection. Specifically, the feature is modeled as an inverse gamma distribution and the target kinematic state is modeled as a Gaussian distribution; the feature is integrated into the target kinematic state to iteratively estimate the target detection probability with the motion state. Our experimental results show that the proposed method outperforms the standard PMBM filter and the robust PMBM filter based on Beta distribution in the scenarios with unknown and time-varying detection probability. Further, we apply the proposed filter to a simulated infrared image to confirm the effectiveness and robustness of the filter.
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spelling pubmed-91431922022-05-29 Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation Wang, Yi Rao, Peng Chen, Xin Sensors (Basel) Article This paper provides a solution for multi-target tracking with unknown detection probability. For the standard Poisson Multi-Bernoulli Mixture (PMBM) filter, the detection probability is generally considered a priori. However, affected by sensors, the features used for detection, and other environmental factors, the detection probability is time-varying and unknown in most multi-target tracking scenarios. Therefore, the standard PMBM filter is not feasible in practical scenarios. In order to overcome these practical restrictions, we improve the PMBM filter with unknown detection probability using the feature used for detection. Specifically, the feature is modeled as an inverse gamma distribution and the target kinematic state is modeled as a Gaussian distribution; the feature is integrated into the target kinematic state to iteratively estimate the target detection probability with the motion state. Our experimental results show that the proposed method outperforms the standard PMBM filter and the robust PMBM filter based on Beta distribution in the scenarios with unknown and time-varying detection probability. Further, we apply the proposed filter to a simulated infrared image to confirm the effectiveness and robustness of the filter. MDPI 2022-05-13 /pmc/articles/PMC9143192/ /pubmed/35632139 http://dx.doi.org/10.3390/s22103730 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wang, Yi
Rao, Peng
Chen, Xin
Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation
title Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation
title_full Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation
title_fullStr Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation
title_full_unstemmed Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation
title_short Robust PMBM Filter with Unknown Detection Probability Based on Feature Estimation
title_sort robust pmbm filter with unknown detection probability based on feature estimation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9143192/
https://www.ncbi.nlm.nih.gov/pubmed/35632139
http://dx.doi.org/10.3390/s22103730
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