Triangulation of 3D Target Points from Radar Range and Bearing Data
Oskarsson, Magnus LU
(2025)
2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025
p.7314-7323
- Abstract
We propose a method for estimating the 3D position of a target point, given multiple measurements of it, using mm-wave radar data. Given azimuth headings and range estimates from posed radar positions, we find the 3D position, using an approximate, but geometrically and statistically meaningful cost. The 3D position is found in an optimal way, using this approximate cost. By deriving the Lagrangian of the corresponding maximum likelihood and maximum a posteriori estimates, we show that we can find all local minima by solving an eigenvalue problem. The global optimum can then easily and efficiently be extracted from these solutions. We validate the method on synthetic data and test it on several real world datasets, and release public... (More)
We propose a method for estimating the 3D position of a target point, given multiple measurements of it, using mm-wave radar data. Given azimuth headings and range estimates from posed radar positions, we find the 3D position, using an approximate, but geometrically and statistically meaningful cost. The 3D position is found in an optimal way, using this approximate cost. By deriving the Lagrangian of the corresponding maximum likelihood and maximum a posteriori estimates, we show that we can find all local minima by solving an eigenvalue problem. The global optimum can then easily and efficiently be extracted from these solutions. We validate the method on synthetic data and test it on several real world datasets, and release public code11https://github.com/hamburgerlady/radar-triangulation.
(Less)
- author
- Oskarsson, Magnus
LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025
- pages
- 10 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025
- conference location
- Honolulu, United States
- conference dates
- 2025-10-19 - 2025-10-20
- external identifiers
-
- scopus:105035224065
- ISBN
- 9798331589882
- DOI
- 10.1109/ICCVW69036.2025.00753
- language
- English
- LU publication?
- yes
- id
- 952c2ec0-75e5-4a8e-9512-6c1868c40130
- date added to LUP
- 2026-05-13 11:48:39
- date last changed
- 2026-05-13 11:48:49
@inproceedings{952c2ec0-75e5-4a8e-9512-6c1868c40130,
abstract = {{<p>We propose a method for estimating the 3D position of a target point, given multiple measurements of it, using mm-wave radar data. Given azimuth headings and range estimates from posed radar positions, we find the 3D position, using an approximate, but geometrically and statistically meaningful cost. The 3D position is found in an optimal way, using this approximate cost. By deriving the Lagrangian of the corresponding maximum likelihood and maximum a posteriori estimates, we show that we can find all local minima by solving an eigenvalue problem. The global optimum can then easily and efficiently be extracted from these solutions. We validate the method on synthetic data and test it on several real world datasets, and release public code<sup>1</sup><sup>1</sup>https://github.com/hamburgerlady/radar-triangulation.</p>}},
author = {{Oskarsson, Magnus}},
booktitle = {{Proceedings - 2025 IEEE/CVF International Conference on Computer Vision Workshops, ICCV-W 2025}},
isbn = {{9798331589882}},
language = {{eng}},
pages = {{7314--7323}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Triangulation of 3D Target Points from Radar Range and Bearing Data}},
url = {{http://dx.doi.org/10.1109/ICCVW69036.2025.00753}},
doi = {{10.1109/ICCVW69036.2025.00753}},
year = {{2025}},
}