Reliable Orientation Estimation of Vehicles in High-Resolution Radar Images

With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehi...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on microwave theory and techniques Vol. 64; no. 9; pp. 2986 - 2993
Main Authors: Roos, Fabian, Kellner, Dominik, Dickmann, Jurgen, Waldschmidt, Christian
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Automotive radar
bounding box estimation
dimension extraction
Doppler radar
Errors
Estimation
Filtering
Filtration
Imaging radar
Mathematical models
MIMO radar
Orientation
orientation estimation
Radar imaging
radar signal processing
reliability
Sensors
Signal processing algorithms
Vehicles
ISSN:0018-9480, 1557-9670
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:With new generations of high-resolution imaging radars, the orientation of vehicles can be estimated without temporal filtering. This enables time-critical systems to respond even faster. Based on a large data set, this paper compares three generic algorithms for the orientation estimation of a vehicle. An experimental MIMO imaging radar is used to highlight the requirements of a robust algorithm. The well-known orientated bounding box and the so-called L-fit are adapted for radar measurements and compared with a brute-force approach. A quality function selects the best fitted model and is a key factor to minimize alignment errors. Moreover, the reliability of the estimation is evaluated with respect to the aspect angle, the distance to the target, and the number of sensors. An approach to estimate the reliability of the current orientation estimation is introduced. It is shown that the root mean square error of the orientation estimation is 9.77° and 38% smaller compared with the common algorithm. In 50% of the evaluated measurements the orientation estimation error is smaller than 3.73°.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0018-9480
1557-9670
DOI:10.1109/TMTT.2016.2586476