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

1 August 2008

conference paper
Published by Institute of Electrical and Electronics Engineers (IEEE)

Vol. 3719, 189-194
https://doi.org/10.1109/mfi.2008.4648063

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 objectspsila 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.

Keywords

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