A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds
Hough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measu...
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| Format: | Article |
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MDPI AG
2019-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/4/881 |
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| author | Bo Yan Na Xu Wen-Bo Zhao Lu-Ping Xu |
| author_facet | Bo Yan Na Xu Wen-Bo Zhao Lu-Ping Xu |
| author_sort | Bo Yan |
| collection | DOAJ |
| description | Hough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measurements, i.e., a two-dimensional positional information and its measuring time. For taking the full merits of a multi-scan, the measuring time is regarded as a variable for the time axis. This correspondence extends the HT to 3-dimensional data. Meanwhile, a three-dimensional accumulator matrix is built for the purpose of voting. The voting process is done in an iterative way by selecting the 3D-line with the most votes and removing the corresponding measurements in each step. The three dimensional Hough Transform-based extended target track-before-detect technique (3DHT-ET-TBD), proposed here, is suitable to track the extended target and non-extended target simultaneously and few false alarm trajectories arise. Both the real data and simulated data are exploited to evaluate its performance. Compared with the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter based methods and a 4DHT-TBD algorithm, the 3DHT-ET-TBD is a more promising approach for multi-extended target tracking problems due to its high efficiency and low computation, especially in situations where the noise and false alarms are considerably high but few measurements are generated by the extended targets. |
| first_indexed | 2024-04-11T22:32:49Z |
| format | Article |
| id | doaj.art-4f537e39ae3e40dfb6af411148176116 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:32:49Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
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| spelling | doaj.art-4f537e39ae3e40dfb6af4111481761162022-12-22T03:59:18ZengMDPI AGSensors1424-82202019-02-0119488110.3390/s19040881s19040881A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter BackgroundsBo Yan0Na Xu1Wen-Bo Zhao2Lu-Ping Xu3School of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi’an 710126, ChinaSchool of Life Sciences and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi’an 710126, ChinaSchool of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi’an 710126, ChinaSchool of Aerospace Science and Technology, XIDIAN University, 266 Xinglong Section of Xifeng Road, Xi’an 710126, ChinaHough Transform (HT), which has a low sensitivity to local faults and good ability in suppressing noise and clutters, usually applies to trajectory detection in a cluttered environment. This paper describes its application for detecting the trajectories of extended targets in three-dimensional measurements, i.e., a two-dimensional positional information and its measuring time. For taking the full merits of a multi-scan, the measuring time is regarded as a variable for the time axis. This correspondence extends the HT to 3-dimensional data. Meanwhile, a three-dimensional accumulator matrix is built for the purpose of voting. The voting process is done in an iterative way by selecting the 3D-line with the most votes and removing the corresponding measurements in each step. The three dimensional Hough Transform-based extended target track-before-detect technique (3DHT-ET-TBD), proposed here, is suitable to track the extended target and non-extended target simultaneously and few false alarm trajectories arise. Both the real data and simulated data are exploited to evaluate its performance. Compared with the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter based methods and a 4DHT-TBD algorithm, the 3DHT-ET-TBD is a more promising approach for multi-extended target tracking problems due to its high efficiency and low computation, especially in situations where the noise and false alarms are considerably high but few measurements are generated by the extended targets.https://www.mdpi.com/1424-8220/19/4/881extended target trackingtrack-before-detectHough Transform |
| spellingShingle | Bo Yan Na Xu Wen-Bo Zhao Lu-Ping Xu A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds Sensors extended target tracking track-before-detect Hough Transform |
| title | A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds |
| title_full | A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds |
| title_fullStr | A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds |
| title_full_unstemmed | A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds |
| title_short | A Three-Dimensional Hough Transform-Based Track-Before-Detect Technique for Detecting Extended Targets in Strong Clutter Backgrounds |
| title_sort | three dimensional hough transform based track before detect technique for detecting extended targets in strong clutter backgrounds |
| topic | extended target tracking track-before-detect Hough Transform |
| url | https://www.mdpi.com/1424-8220/19/4/881 |
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