Surface Classification with Millimeter-Wave Radar Using Temporal Features and Machine Learning
Montgomery, David ; Holmen, Gustav ; Almers, Peter and Jakobsson, Andreas LU
(2019)
European Radar Conference
- Abstract
- Classification of surfaces using millimeter-wave radar commonly considers the use of polarization-based methods for road condition monitoring. When a surface consists of larger structures, one is instead often interested in monitoring the surface topography, which is typically not resolvable by the limited radar bandwidth. To alleviate this problem, we here consider several phase coherent radar measurments conducted during the motion of the radar, in order to capture not only the instantaneous depth measurement, but also the depth variation over time. Analysis of scattering from rough surfaces is highly complex and relies on prior knowledge of surface structure. By constructing a set of features based on a number of radar measurements over... (More)
- Classification of surfaces using millimeter-wave radar commonly considers the use of polarization-based methods for road condition monitoring. When a surface consists of larger structures, one is instead often interested in monitoring the surface topography, which is typically not resolvable by the limited radar bandwidth. To alleviate this problem, we here consider several phase coherent radar measurments conducted during the motion of the radar, in order to capture not only the instantaneous depth measurement, but also the depth variation over time. Analysis of scattering from rough surfaces is highly complex and relies on prior knowledge of surface structure. By constructing a set of features based on a number of radar measurements over time, a machine learning classifier is proposed to distinguish grass target surfaces from asphalt, gravel, soil, and tiled surfaces. Six different classifier structures are evauated and presented in the paper. Using estimated autocovariances and average envelope shapes as features, a small, fully connected, neural network classifier is, using a leave-one-out strategy, shown to allow for accurate determination of the surface type. The proposed classifier can be implemented with limited hardware requirements, making it suitable for autonomous devices, such as, e.g., autonomous lawn mowers. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/6cfa97f5-8081-4306-bfd7-e61b9a17af30
- author
- Montgomery, David
; Holmen, Gustav
; Almers, Peter
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2019-11
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- millimeter-wave radar, machine learning, classification of rough surfaces
- host publication
- 2019 16th European Radar Conference (EuRAD)
- article number
- 8904699
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- European Radar Conference
- conference location
- Paris, France
- conference dates
- 2019-10-02 - 2019-10-04
- external identifiers
-
- scopus:85076221166
- ISBN
- 978-1-7281-3733-9
- 978-2-87487-057-6
- language
- English
- LU publication?
- yes
- id
- 6cfa97f5-8081-4306-bfd7-e61b9a17af30
- alternative location
- https://ieeexplore.ieee.org/document/8904699
- date added to LUP
- 2019-05-31 15:38:38
- date last changed
- 2024-03-19 11:07:43
@inproceedings{6cfa97f5-8081-4306-bfd7-e61b9a17af30,
abstract = {{Classification of surfaces using millimeter-wave radar commonly considers the use of polarization-based methods for road condition monitoring. When a surface consists of larger structures, one is instead often interested in monitoring the surface topography, which is typically not resolvable by the limited radar bandwidth. To alleviate this problem, we here consider several phase coherent radar measurments conducted during the motion of the radar, in order to capture not only the instantaneous depth measurement, but also the depth variation over time. Analysis of scattering from rough surfaces is highly complex and relies on prior knowledge of surface structure. By constructing a set of features based on a number of radar measurements over time, a machine learning classifier is proposed to distinguish grass target surfaces from asphalt, gravel, soil, and tiled surfaces. Six different classifier structures are evauated and presented in the paper. Using estimated autocovariances and average envelope shapes as features, a small, fully connected, neural network classifier is, using a leave-one-out strategy, shown to allow for accurate determination of the surface type. The proposed classifier can be implemented with limited hardware requirements, making it suitable for autonomous devices, such as, e.g., autonomous lawn mowers.}},
author = {{Montgomery, David and Holmen, Gustav and Almers, Peter and Jakobsson, Andreas}},
booktitle = {{2019 16th European Radar Conference (EuRAD)}},
isbn = {{978-1-7281-3733-9}},
keywords = {{millimeter-wave radar; machine learning; classification of rough surfaces}},
language = {{eng}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
title = {{Surface Classification with Millimeter-Wave Radar Using Temporal Features and Machine Learning}},
url = {{https://ieeexplore.ieee.org/document/8904699}},
year = {{2019}},
}