Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking
More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an...
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| Format: | Online |
| Language: | English |
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MDPI
2015
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610512/ |
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pubmed-46105122015-10-26 Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking Zhang, Tao Wu, Renbiao Article More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to accurately and effectively partition the measurements of multiple extended targets remains unsolved. In this paper, affinity propagation clustering is introduced into measurement partitioning for extended target tracking, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering capable of partitioning the measurement in a densely cluttered environment with high accuracy. The Gaussian mixture probability hypothesis density filter is implemented for multiple extended target tracking. Numerical results are presented to demonstrate the performance of the proposed algorithm, which provides improved performance, while obviously reducing the computational complexity. MDPI 2015-09-08 /pmc/articles/PMC4610512/ /pubmed/26370998 http://dx.doi.org/10.3390/s150922646 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Zhang, Tao Wu, Renbiao |
| spellingShingle |
Zhang, Tao Wu, Renbiao Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking |
| author_facet |
Zhang, Tao Wu, Renbiao |
| author_sort |
Zhang, Tao |
| title |
Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking |
| title_short |
Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking |
| title_full |
Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking |
| title_fullStr |
Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking |
| title_full_unstemmed |
Affinity Propagation Clustering of Measurements for Multiple Extended Target Tracking |
| title_sort |
affinity propagation clustering of measurements for multiple extended target tracking |
| description |
More measurements are generated by the target per observation interval, when the target is detected by a high resolution sensor, or there are more measurement sources on the target surface. Such a target is referred to as an extended target. The probability hypothesis density filter is considered an efficient method for tracking multiple extended targets. However, the crucial problem of how to accurately and effectively partition the measurements of multiple extended targets remains unsolved. In this paper, affinity propagation clustering is introduced into measurement partitioning for extended target tracking, and the elliptical gating technique is used to remove the clutter measurements, which makes the affinity propagation clustering capable of partitioning the measurement in a densely cluttered environment with high accuracy. The Gaussian mixture probability hypothesis density filter is implemented for multiple extended target tracking. Numerical results are presented to demonstrate the performance of the proposed algorithm, which provides improved performance, while obviously reducing the computational complexity. |
| publisher |
MDPI |
| publishDate |
2015 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610512/ |
| _version_ |
1613489839560196096 |