A comparison of track-to-track fusion algorithms for automotive sensor fusion

Stephan Matzka, Richard Altendorfer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

In exteroceptive automotive sensor fusion, sensor data are usually only available as processed, tracked object data and not as raw sensor data. Applying a Kalman filter to such data leads to additional delays and generally underestimates the fused objects' covariance due to temporal correlations of individual sensor data as well as inter-sensor correlations. We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion of sensor objects of unknown correlation, namely covariance union, covariance intersection, and use of cross-covariance. For our simulation setup, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load. © 2009 Springer-Verlag Berlin Heidelberg.

Original languageEnglish
Title of host publicationMultisensor Fusion and Integration for Intelligent Systems: An Edition of the Selected Papers from the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems 2008
Pages69-81
Number of pages13
Volume35 LNEE
DOIs
Publication statusPublished - 2009
Event2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems - Seoul, Korea, Republic of
Duration: 20 Aug 200822 Aug 2008

Publication series

NameLecture Notes in Electrical Engineering
Volume35 LNEE
ISSN (Print)1876-1100

Conference

Conference2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
Abbreviated titleMFI
CountryKorea, Republic of
CitySeoul
Period20/08/0822/08/08

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