Robust Localization of Close-Range Radar Reflections
(2023) 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023 p.549-553- Abstract
In order to allow for a computationally efficient estimation of radar reflections, one commonly assumes the reflecting target to be in the far-field of the sensing array, such that the impinging wavefront is modeled as a linear phase-shift along the array. This works well in most radar scenarios, but causes significant performance degradation for close-range radar systems, wherein the curvature of the impinging wavefront may not be neglected. In this work, we examine how the used far-field assumption limits the resulting performance, illustrating how the (misspecified) Cramér-Rao lower bound (CRLB), taking the model mismatch into account, significantly devi-ates from the true CRLB for close-range targets. We further introduce a robust... (More)
In order to allow for a computationally efficient estimation of radar reflections, one commonly assumes the reflecting target to be in the far-field of the sensing array, such that the impinging wavefront is modeled as a linear phase-shift along the array. This works well in most radar scenarios, but causes significant performance degradation for close-range radar systems, wherein the curvature of the impinging wavefront may not be neglected. In this work, we examine how the used far-field assumption limits the resulting performance, illustrating how the (misspecified) Cramér-Rao lower bound (CRLB), taking the model mismatch into account, significantly devi-ates from the true CRLB for close-range targets. We further introduce a robust estimator that allows for the waveform cur-vature, showing that this computationally efficient estimator allows for superior performance for close-range reflectors.
(Less)
- author
- Jansson, Andreas LU and Jakobsson, Andreas LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
- editor
- Matthews, Michael B.
- pages
- 5 pages
- publisher
- IEEE Computer Society
- conference name
- 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023
- conference location
- Pacific Grove, United States
- conference dates
- 2023-10-29 - 2023-11-01
- external identifiers
-
- scopus:85190365073
- ISBN
- 9798350325744
- DOI
- 10.1109/IEEECONF59524.2023.10476947
- language
- English
- LU publication?
- yes
- id
- a87d56be-0a47-4297-94e2-9330a6b37390
- date added to LUP
- 2024-04-29 16:22:12
- date last changed
- 2024-04-29 16:23:23
@inproceedings{a87d56be-0a47-4297-94e2-9330a6b37390, abstract = {{<p>In order to allow for a computationally efficient estimation of radar reflections, one commonly assumes the reflecting target to be in the far-field of the sensing array, such that the impinging wavefront is modeled as a linear phase-shift along the array. This works well in most radar scenarios, but causes significant performance degradation for close-range radar systems, wherein the curvature of the impinging wavefront may not be neglected. In this work, we examine how the used far-field assumption limits the resulting performance, illustrating how the (misspecified) Cramér-Rao lower bound (CRLB), taking the model mismatch into account, significantly devi-ates from the true CRLB for close-range targets. We further introduce a robust estimator that allows for the waveform cur-vature, showing that this computationally efficient estimator allows for superior performance for close-range reflectors.</p>}}, author = {{Jansson, Andreas and Jakobsson, Andreas}}, booktitle = {{Conference Record of the 57th Asilomar Conference on Signals, Systems and Computers, ACSSC 2023}}, editor = {{Matthews, Michael B.}}, isbn = {{9798350325744}}, language = {{eng}}, pages = {{549--553}}, publisher = {{IEEE Computer Society}}, title = {{Robust Localization of Close-Range Radar Reflections}}, url = {{http://dx.doi.org/10.1109/IEEECONF59524.2023.10476947}}, doi = {{10.1109/IEEECONF59524.2023.10476947}}, year = {{2023}}, }