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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9143192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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