Self-tuning multisensor weighted measurement fusion kalman filter

IEEE Transactions on Aerospace and Electronic Systems

Volumn 45, Issue 1, 2009, Pages 179-191

  Gao, Yuan ^a   Jia, Wen Jing ^a   Sun, Xiao Jun ^a   Deng, Zi Li ^a  

^a HEILONGJIANG UNIVERSITY   (China)

Author keywords

Convergence; Correlation; Kalman filters; Noise; Noise measurement; Stochastic processes; Weight measurement

Indexed keywords

CONVERGENCE; CORRELATION; NOISE; NOISE MEASUREMENT; STOCHASTIC PROCESSES; WEIGHT MEASUREMENT;

ACOUSTIC NOISE MEASUREMENT; KALMAN FILTERS; RANDOM PROCESSES; SPURIOUS SIGNAL NOISE; TUNING; WEIGHING;

CONTROL THEORY;

EID: 65449118473     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/TAES.2009.4805272     Document Type: Article

References (28)

1. Bar-Shalom, Y., and Li, X. R. Multitarget-Multisensor Tracking: Principles and Techniques. Storrs, CT: YBS Publishing, 1995.
2. Hashmipour, H. R., Roy, S., and Laub, A. J. Decentralized structures for parallel Kalman filtering. IEEE Transactions on Automatic Control, 33, 1 (1988), 88-93.
3. Chang, K. C., Zhi, T., and Saha, R. K. Performance evaluation of track fusion with imformation matrix filter. IEEE Transactions on Aerospace and Electronic Systems, 38, 2 (2002), 455-465.
4. Zhu, Y., You, Z., Zhao, J., Zhang, K., and Li, X. R. The optimality for the distributed Kalman filtering fusion with feedback. Automatica, 37 (2001), 1489-1493.
5. Carlson, N. A. Federated square root filter for decentralized parallel processes. IEEE Transactions on Aerospace and Electronic Systems, 26, 3(1990), 517-525.
6. Sun, S. L., and Deng, Z. L. Multi-sensor optimal information fusion Kalman filter. Automatica, 80 (2004), 1017-1023.
7. Deng, Z. L., Gao, Y., Mao, L., Li, Y., and Hao, G. New approach to information fusion steady-state Kalman filtering. Automatica, 41 (2005), 1695-1707.
8. Gan, Q., and Harris, C. J. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion. IEEE Transactions on Aerospace and Electronic Systems, 37, 1 (2001), 273-279.
9. Roecker, J. A., and McGillem, C. D. Comparison of two-sensor tracking method based on state vector fusion and measurement fusion. IEEE Transactions on Aerospace and Electronic Systems, 24, 4 (2001), 447-449.
10. Gao, J. B., and Harris, C. J. Some remarks on Kalman filters for the multisensor fusion. Information Fusion, 3 (2002), 191-201.
11. Yu, A. X., Hu, D. W., and Zhou, W. H. Performance comparison of multisensor measurement fusion algorithms. Journal of National University of Defense Technology, 25, 6 (2003), 39-44.
12. Deng, Z. L. On functional equivalence of two measurement fusion methods. Control Theory & Applications, 23, 2 (2006), 319-323.
13. Deng, Z. L., Hao, G., and Wu X. H. Global optimality and completely functional equivalence of two weighted measurement fusion algorithms. Science Technology and Engineering, 5, 13 (2005), 860-865.
14. Ou, L. J., Qiu, H. Z., and Zhang, H. Y. Multiple correlated measurements fusion algorithm and its optimality. Information and Control, 34, 6 (2005), 690-695.
15. Hagander, P., and Wittenmark, B. A self-tuning filter for fixed-lag smoothing. IEEE Transactions on Information Theory, 23, 3 (1977), 377-384.
16. Moir, T., and Grimble, M. J. Optimal self-tuning filtering, prediction, and smoothing for multivariable processes. IEEE Transactions on Automatic Control, 29, 2 (1984), 128-137.
17. Deng, Z. L., Zhang, H. S., Liu, S. J., and Zhou, L. Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems. Automatica, 32, 2 (1996), 199-216.
18. Deng, Z. L. Self-Tuning filtering Theory and its Applications-Modern Time Series Analysis Method. Harbin, China: Harbin Institute of Technology Press, 2003, 118-159.
19. Chui, C. K., and Chen, G. Kalman Filtering with Real-Time Applications. New York: Springer-Verlag, 1987.
20. Kailath, T., Sayed, A. H., and Hassibi, B. Linear Estimation. Upper Saddle River, NJ: Prentice-Hall, 2000.
21. Kailath, T. Linear Systems. Englewood Cliffs, NJ: Prentice-Hall, 1986.
22. Ljung, L. System Identification, Theory for the User (2nd ed.). Beijing, China: Tsinghua University Press, 2002.
23. Rudin, W. Principles of Mathematical Analysis (3rd ed.). Beijing, China: China Machine Press, 2004.
24. Mehra, R. K. On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control, 15, 2 (1970), 175-184.
25. Lee, T. A direct approach to identify the noise covariances of Kalman filtering. IEEE Transactions on Automatic Control, 25 (1980), 841-842.
26. Sage, A. P., and Husa, G. W. Adaptive filtering with unknown prior statistics. In Proceedings of Joint Automatic Control Conference, 1969, 769-769.
27. Karlin, S., and Taylor, H. M. A First Course in Stochastic Processes (2nd ed.). New York: Academic Press, 1975.
28. Horm, R. A., and Johnson, C. R. Matrix Analysis. Cambridge, UK: Cambridge University Press, 1985.