An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion
In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is propos...
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2015-01-01
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Series: | Journal of Spectroscopy |
Online Access: | http://dx.doi.org/10.1155/2015/179039 |
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doaj-b113a8cd9db2464c8b4624cb8d7039e22020-11-24T22:12:49ZengHindawi LimitedJournal of Spectroscopy2314-49202314-49392015-01-01201510.1155/2015/179039179039An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data FusionGuoliang Zhang0Chunling Yang1Yan Zhang2School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaSchool of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, ChinaIn order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter. Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets. Spectral fusion produces the appropriate binary detection model and the computational probability of detection. Secondly, the particle filtering-based adaptive PHD algorithm is developed to detect and track multiple targets. This algorithm can deal with the nonlinear measurement on target state. In addition, the calculated probability of detection substitutes the fixed detection probability in PHD filter. Finally, the synthetic data sets based on various actual background images were utilized to validate the effectiveness of the detection approach. The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances.http://dx.doi.org/10.1155/2015/179039 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Guoliang Zhang Chunling Yang Yan Zhang |
spellingShingle |
Guoliang Zhang Chunling Yang Yan Zhang An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion Journal of Spectroscopy |
author_facet |
Guoliang Zhang Chunling Yang Yan Zhang |
author_sort |
Guoliang Zhang |
title |
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion |
title_short |
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion |
title_full |
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion |
title_fullStr |
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion |
title_full_unstemmed |
An Adaptive PHD Filter for Multitarget Tracking with Multispectral Data Fusion |
title_sort |
adaptive phd filter for multitarget tracking with multispectral data fusion |
publisher |
Hindawi Limited |
series |
Journal of Spectroscopy |
issn |
2314-4920 2314-4939 |
publishDate |
2015-01-01 |
description |
In order to improve the detection and tracking performance of multiple targets from IR multispectral image sequences, the approach based on spectral fusion algorithm and adaptive probability hypothesis density (PHD) filter is proposed. Firstly, the nonstationary adaptive suppression method is proposed to remove the background clutter. Based on the multispectral image sequence, the spectral fusion method is used to detect the abnormal targets. Spectral fusion produces the appropriate binary detection model and the computational probability of detection. Secondly, the particle filtering-based adaptive PHD algorithm is developed to detect and track multiple targets. This algorithm can deal with the nonlinear measurement on target state. In addition, the calculated probability of detection substitutes the fixed detection probability in PHD filter. Finally, the synthetic data sets based on various actual background images were utilized to validate the effectiveness of the detection approach. The results demonstrate that the proposed approach outperforms the conventional sequential PHD filtering in terms of detection and tracking performances. |
url |
http://dx.doi.org/10.1155/2015/179039 |
work_keys_str_mv |
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1725802370797928448 |