Tracking Algorithms Aided by the Pose of Target Article Swipe
Dai Liu
,
Yongbo Zhao
,
Baoqing Xu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2890981
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1109/access.2019.2890981
The traditional target tracking algorithms have utilized the information on the target position. With the development of radar high-resolution technology, it is possible to obtain the pose of target. In this paper, two target tracking algorithms aided by the pose of target are proposed. First, the pose of the target is estimated in the real time by the high-resolution range profile, and then, the pose is added to the target measurement equation. Because the relationship between the pose and the motion parameters of the targets is nonlinear, the extended Kalman filter algorithm aided by the pose of target (Pose-EKF) and the unscented Kalman filter algorithm aided by the pose of target (Pose-UKF) are proposed. The results of simulation demonstrate that compared with the traditional extended Kalman filter algorithm (EKF) and the traditional Unscented Kalman filter algorithm (UKF), the proposed algorithm can greatly improve the target tracking accuracy (position precision and velocity precision) and the convergence speed. The pose measurement error has a little effect on the tracking performance. The difference in the tracking accuracy between Pose-EKF and Pose-UKF is very little. But the Pose-EKF is better than Pose-UKF in terms of computation time, but Pose-EKF fails and Pose-UKF is effective when the pose is critical.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2019.2890981
- https://ieeexplore.ieee.org/ielx7/6287639/8600701/08604001.pdf
- OA Status
- gold
- Cited By
- 5
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2908799008
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2908799008Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/access.2019.2890981Digital Object Identifier
- Title
-
Tracking Algorithms Aided by the Pose of TargetWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-01-01Full publication date if available
- Authors
-
Dai Liu, Yongbo Zhao, Baoqing XuList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2019.2890981Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/8600701/08604001.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/8600701/08604001.pdfDirect OA link when available
- Concepts
-
Extended Kalman filter, Pose, Computer science, Kalman filter, Computer vision, Artificial intelligence, 3D pose estimation, Tracking (education), Algorithm, Articulated body pose estimation, Position (finance), Finance, Economics, Pedagogy, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2023: 1, 2020: 2, 2019: 1Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.on | 9, 164 |
| abstract_inverted_index.to | 23, 67 |
| abstract_inverted_index.But | 181 |
| abstract_inverted_index.The | 0, 114, 156, 168 |
| abstract_inverted_index.and | 61, 78, 99, 129, 149, 152, 176, 196 |
| abstract_inverted_index.are | 42, 112 |
| abstract_inverted_index.but | 193 |
| abstract_inverted_index.can | 140 |
| abstract_inverted_index.has | 160 |
| abstract_inverted_index.the | 7, 10, 14, 25, 38, 45, 48, 53, 57, 63, 68, 73, 76, 79, 83, 87, 94, 100, 107, 122, 130, 137, 143, 153, 165, 171, 182, 201 |
| abstract_inverted_index.two | 32 |
| abstract_inverted_index.With | 13 |
| abstract_inverted_index.have | 5 |
| abstract_inverted_index.pose | 26, 39, 46, 64, 77, 95, 108, 157, 202 |
| abstract_inverted_index.real | 54 |
| abstract_inverted_index.than | 186 |
| abstract_inverted_index.that | 119 |
| abstract_inverted_index.this | 30 |
| abstract_inverted_index.time | 55 |
| abstract_inverted_index.very | 179 |
| abstract_inverted_index.when | 200 |
| abstract_inverted_index.with | 121 |
| abstract_inverted_index.(EKF) | 128 |
| abstract_inverted_index.added | 66 |
| abstract_inverted_index.aided | 36, 92, 105 |
| abstract_inverted_index.error | 159 |
| abstract_inverted_index.fails | 195 |
| abstract_inverted_index.radar | 17 |
| abstract_inverted_index.range | 59 |
| abstract_inverted_index.terms | 189 |
| abstract_inverted_index.then, | 62 |
| abstract_inverted_index.time, | 192 |
| abstract_inverted_index.(UKF), | 136 |
| abstract_inverted_index.First, | 44 |
| abstract_inverted_index.Kalman | 89, 102, 125, 133 |
| abstract_inverted_index.better | 185 |
| abstract_inverted_index.effect | 163 |
| abstract_inverted_index.filter | 90, 103, 126, 134 |
| abstract_inverted_index.little | 162 |
| abstract_inverted_index.motion | 80 |
| abstract_inverted_index.obtain | 24 |
| abstract_inverted_index.paper, | 31 |
| abstract_inverted_index.speed. | 155 |
| abstract_inverted_index.target | 2, 11, 33, 41, 49, 69, 97, 110, 144 |
| abstract_inverted_index.Because | 72 |
| abstract_inverted_index.between | 75, 174 |
| abstract_inverted_index.greatly | 141 |
| abstract_inverted_index.improve | 142 |
| abstract_inverted_index.little. | 180 |
| abstract_inverted_index.results | 115 |
| abstract_inverted_index.target. | 28 |
| abstract_inverted_index.targets | 84 |
| abstract_inverted_index.Pose-EKF | 175, 183, 194 |
| abstract_inverted_index.Pose-UKF | 177, 187, 197 |
| abstract_inverted_index.accuracy | 146, 173 |
| abstract_inverted_index.compared | 120 |
| abstract_inverted_index.extended | 88, 124 |
| abstract_inverted_index.possible | 22 |
| abstract_inverted_index.profile, | 60 |
| abstract_inverted_index.proposed | 138 |
| abstract_inverted_index.tracking | 3, 34, 145, 166, 172 |
| abstract_inverted_index.utilized | 6 |
| abstract_inverted_index.velocity | 150 |
| abstract_inverted_index.(position | 147 |
| abstract_inverted_index.Unscented | 132 |
| abstract_inverted_index.algorithm | 91, 104, 127, 135, 139 |
| abstract_inverted_index.critical. | 204 |
| abstract_inverted_index.effective | 199 |
| abstract_inverted_index.equation. | 71 |
| abstract_inverted_index.estimated | 51 |
| abstract_inverted_index.position. | 12 |
| abstract_inverted_index.precision | 148 |
| abstract_inverted_index.proposed. | 43, 113 |
| abstract_inverted_index.unscented | 101 |
| abstract_inverted_index.(Pose-EKF) | 98 |
| abstract_inverted_index.(Pose-UKF) | 111 |
| abstract_inverted_index.algorithms | 4, 35 |
| abstract_inverted_index.difference | 169 |
| abstract_inverted_index.nonlinear, | 86 |
| abstract_inverted_index.parameters | 81 |
| abstract_inverted_index.precision) | 151 |
| abstract_inverted_index.simulation | 117 |
| abstract_inverted_index.computation | 191 |
| abstract_inverted_index.convergence | 154 |
| abstract_inverted_index.demonstrate | 118 |
| abstract_inverted_index.development | 15 |
| abstract_inverted_index.information | 8 |
| abstract_inverted_index.measurement | 70, 158 |
| abstract_inverted_index.technology, | 19 |
| abstract_inverted_index.traditional | 1, 123, 131 |
| abstract_inverted_index.performance. | 167 |
| abstract_inverted_index.relationship | 74 |
| abstract_inverted_index.high-resolution | 18, 58 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.69953162 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |