Lab Meeting March 23, 2011 (Shao-Chen): A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008)

Title: A Comparison of Track-to-Track Fusion Algorithms for Automotive Sensor Fusion (MFI2008, Multisensor Fusion and Integration for Intelligent Systems)

Authors: Stephan Matzka and Richard Altendorfer

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.

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