Multi-sensor object tracking performance limits by the Cramer-Rao lower bound

Joris Domhof; Riender Happee; Pieter Jonker

doi:10.23919/ICIF.2017.8009637

Multi-sensor object tracking performance limits by the Cramer-Rao lower bound

Joris Domhof, Riender Happee, Pieter Jonker

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

6 Citations (Scopus)

Abstract

This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.

Original language	English
Title of host publication	Proceedings of the 20th International Conference on Information Fusion
Place of Publication	Piscataway, NJ, USA
Publisher	IEEE
Number of pages	8
ISBN (Print)	978-0-9964-5270-0
DOIs	https://doi.org/10.23919/ICIF.2017.8009637
Publication status	Published - 2017
Event	20th International Conference on Information Fusion - Xi'an, China Duration: 10 Jul 2017 → 13 Jul 2017

Conference

Conference	20th International Conference on Information Fusion
Country/Territory	China
City	Xi'an
Period	10/07/17 → 13/07/17

Keywords

Radar tracking
Laser radar
Covariance matrices
Cameras
Automobiles
Measurement uncertainty

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10.23919/ICIF.2017.8009637

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@inproceedings{415399172baf485dbac4aed0a9dc2613,

title = "Multi-sensor object tracking performance limits by the Cramer-Rao lower bound",

abstract = "This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.",

keywords = "Radar tracking, Laser radar, Covariance matrices, Cameras, Automobiles, Measurement uncertainty",

author = "Joris Domhof and Riender Happee and Pieter Jonker",

year = "2017",

doi = "10.23919/ICIF.2017.8009637",

language = "English",

isbn = "978-0-9964-5270-0",

booktitle = "Proceedings of the 20th International Conference on Information Fusion",

publisher = "IEEE",

address = "United States",

note = "20th International Conference on Information Fusion ; Conference date: 10-07-2017 Through 13-07-2017",

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TY - GEN

T1 - Multi-sensor object tracking performance limits by the Cramer-Rao lower bound

AU - Domhof, Joris

AU - Happee, Riender

AU - Jonker, Pieter

PY - 2017

Y1 - 2017

N2 - This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.

AB - This paper presents a systematic approach to evaluate the tracking performance limits for different sensor modalities (lidar, radar and vision) and for combination of these sensors modalities. The Cramer-Rao lower bound (CRLB) is used to predict the tracking performance limits for state of the art sensors such as the Continental ARS408 radar, Velodyne HDL-64E lidar and a state of the art monocular/stereo camera. The performance is evaluated by computing the theoretical CRLB in urban and highway environments. In both scenarios, the best performance was achieved by a combination of lidar and radar. In the close range, stereo vision improves the longitudinal tracking performance limits. Furthermore, radar is crucial on highways because of the quick longitudinal convergence characteristics.

KW - Radar tracking

KW - Laser radar

KW - Covariance matrices

KW - Cameras

KW - Automobiles

KW - Measurement uncertainty

U2 - 10.23919/ICIF.2017.8009637

DO - 10.23919/ICIF.2017.8009637

M3 - Conference contribution

SN - 978-0-9964-5270-0

BT - Proceedings of the 20th International Conference on Information Fusion

PB - IEEE

CY - Piscataway, NJ, USA

T2 - 20th International Conference on Information Fusion

Y2 - 10 July 2017 through 13 July 2017

ER -

Multi-sensor object tracking performance limits by the Cramer-Rao lower bound

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Keywords

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