Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion

Currently there exist two commonly used measurement fusion methods for Kalman-filter-based multisensor data fusion. The first (Method I) simply merges the multisensor data through the observation vector of the Kalman filter, whereas the second (Method II) combines the multisensor data based on a minimum-mean-square-error criterion. This paper, based on an analysis of the fused state estimate covariances of the two measurement fusion methods, shows that the two measurement fusion methods are functionally equivalent if the sensors used for data fusion, with different and independent noise characteristics, have identical measurement matrices. Also presented are simulation results on state estimation using the two measurement fusion methods, followed by the analysis of the computational advantages of each method.

[1]  J. A. Roecker,et al.  Comparison of two-sensor tracking methods based on state vector fusion and measurement fusion , 1988 .

[2]  Hugh Durrant-Whyte,et al.  Data Fusion and Sensor Management: A Decentralized Information-Theoretic Approach , 1995 .

[3]  Yakov Bar-Shalom,et al.  Multitarget-Multisensor Tracking: Principles and Techniques , 1995 .

[4]  Y. Bar-Shalom,et al.  On optimal track-to-track fusion , 1997, IEEE Transactions on Aerospace and Electronic Systems.

[5]  R.K. Saha,et al.  Track-to-track fusion with dissimilar sensors , 1996, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Y. Bar-Shalom.  On the track-to-track correlation problem , 1981 .

[7]  Zhi Tian,et al.  Performance evaluation of track fusion with information matrix filter , 2002 .

[8]  Yaakov Bar-Shalom,et al.  The Effect of the Common Process Noise on the Two-Sensor Fused-Track Covariance , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[9]  R. Saha,et al.  An efficient algorithm for multisensor track fusion , 1998 .

[10]  Y. Bar-Shalom.  Tracking and data association , 1988 .

[11]  Christopher J. Harris,et al.  Fuzzy local linearization and local basis function expansion in nonlinear system modeling , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Q. Gan,et al.  Neurofuzzy state estimators using a modified ASMOD and Kalman filter algorithm , 1999 .

[13]  Amir Averbuch,et al.  Interacting Multiple Model Methods in Target Tracking: A Survey , 1988 .

[14]  Chris J. Harris,et al.  Multi-sensor data fusion for helicopter guidance using neuro-fuzzy estimation algorithms , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.