Asynchronous sensor fusion using multi-rate Kalman filter

Young Seop Son, Wonhee Kim, Seung Hi Lee, Chung Choo Chung

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We propose a multi-rate sensor fusion of vision and radar using Kalman filter to solve problems of asynchronized and multi-rate sampling periods in object vehicle tracking. A model based prediction of object vehicles is performed with a decentralized multi-rate Kalman filter for each sensor (vision and radar sensors.) To obtain the improvement in the performance of position prediction, different weighting is applied to each sensor's predicted object position from the multi-rate Kalman filter. The proposed method can provide estimated position of the object vehicles at every sampling time of ECU. The Mahalanobis distance is used to make correspondence among the measured and predicted objects. Through the experimental results, we validate that the post-processed fusion data give us improved tracking performance. The proposed method obtained two times improvement in the object tracking performance compared to single sensor method (camera or radar sensor) in the view point of roots mean square error.

Original languageEnglish
Pages (from-to)1551-1558
Number of pages8
JournalTransactions of the Korean Institute of Electrical Engineers
Issue number11
StatePublished - 1 Nov 2014


  • Kalman filter
  • Multi-rate
  • Object vehicle tracking
  • Sensor fusion


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