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

36 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 the simulation setup used in this paper, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load. ©2008 IEEE.

Original languageEnglish
Title of host publicationIEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems 2008
Pages189-194
Number of pages6
ISBN (Electronic)978-1-4244-2144-2
DOIs
Publication statusPublished - 2008
Event2008 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems - Seoul, Korea, Republic of
Duration: 20 Aug 200822 Aug 2008

Conference

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

Fingerprint Dive into the research topics of 'A comparison of track-to-track fusion algorithms for automotive sensor fusion'. Together they form a unique fingerprint.

  • Cite this

    Matzka, S., & Altendorfer, R. (2008). A comparison of track-to-track fusion algorithms for automotive sensor fusion. In IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems 2008 (pp. 189-194) https://doi.org/10.1109/MFI.2008.4648063