Self-Tuning decoupled fusion Kalman predictor and its convergence analysis

IEEE Sensors Journal

Volumn 9, Issue 12, 2009, Pages 2024-2032

  Ran, Chenjian ^a   Tao, Guili ^a   Liu, Jinfang ^a   Deng, Zili ^a  

^a HEILONGJIANG UNIVERSITY   (China)

Author keywords

Convergence; Dynamic variance error system analysis (DVESA) method; Information fusion noise variance estimation; Multisensor information fusion; Self tuning Riccati equation; Self tuning weighted fusion Kalman predictor

Indexed keywords

A-STABILITY; ASYMPTOTIC OPTIMALITY; CONVERGENCE; CONVERGENCE ANALYSIS; CONVERGENCE PROBLEMS; DECISION CRITERIONS; DECOUPLED FUSION; FUSION RULE; KALMAN PREDICTOR; LEAST SQUARE; LYAPUNOV EQUATION; MULTI-SENSOR INFORMATION FUSION; MULTI-SENSOR SYSTEMS; NOISE VARIANCE; OPTIMAL FUSION; SELF-TUNING RICCATI EQUATION; SELF-TUNING WEIGHTED FUSION KALMAN PREDICTOR; SELFTUNING; SIMULATION EXAMPLE; STATE COMPONENT; TRACKING SYSTEM; VARIANCE ERROR;

ESTIMATION; GAUSSIAN NOISE (ELECTRONIC); INFORMATION FUSION; LYAPUNOV FUNCTIONS; LYAPUNOV METHODS; OPTIMIZATION; RICCATI EQUATIONS; SYSTEMS ANALYSIS;

LEAST SQUARES APPROXIMATIONS;

EID: 77952039170     PISSN: 1530437X     EISSN: None     Source Type: Journal    
DOI: 10.1109/JSEN.2009.2033260     Document Type: Article

References (16)

1. Z. L. Deng, Y. Gao, L. Mao, Y. Li, and G. Hao, "New approach to information fusion steady-state Kalman filtering," Automatica, vol. 41, pp. 1695-1770, 2005.
2. S. L. Sun and Z. L. Deng, "Multi-sensor optimal information fusion Kalman filter," Automatica, vol. 40, pp. 1017-1023, 2004.
3. Z. L. Deng, Y. Gao, C. B. Li, and G. Hao, "Self-tuning decoupled information fusion Wiener state component filters and their convergence," Automatica, vol. 44, pp. 685-695, 2008. (Pubitemid 351273697)
4. X. J. Sun, P. Zhang, and Z. L. Deng, "Self-tuning decoupled fusion Kalman filter based on Riccati equation," Frontiers of Electronic and Electronic Engineering in Chinese, vol. 3, no. 4, pp. 459-464, 2008.
5. R. K. Mehra, "On the identification of variances and adaptive Kalman filtering," IEEE Trans. Autom. Control, vol. AC-15, no. 2, pp. 175-184, 1970.
6. Z. L. Deng, H. S. Zhang, S. J. Liu, and L. Zhou, "Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problem," Automatica, vol. 32, no. 2, pp. 199-216, 1996. (Pubitemid 126349315)
7. P. Hagander and B. Wittenmark, "A self-tuning filter for fixed-lag smoothing," IEEE Trans. Inform. Theory, vol. IT-23, pp. 377-384, 1977.
8. P. T. K. Fung and M. J. Grimble, "Dynamic ship positioning using a self-tuning Kalman ilter," IEEE Trans. Autom. Control, vol. AC-28, pp. 339-349, 1983.
9. T. Moir and M. J. Grimble, "Optimal self-tuning iltering, prediction, and smoothering for discrete multivariable processes," IEEE Trans. Autom. Control, vol. AC-29, pp. 128-137, 1984. (Pubitemid 14570745)
10. Z. L. Deng and C. B. Li, "Self-tuning information fusion Kalman predictor weighted by diagonal matrices and its convergence analysis," Acta Automatic Sinica, vol. 33, no. 2, pp. 156-163, 2007.
11. Y. Gao, W. J. Jia, X. J. Sun, and Z. L. Deng, "Self-tuning multisensor weighted measurement fusion Kalman ilter," IEEE Trans. Aerosp. Electron. Syst., vol. 45, no. 1, pp. 179-191, 2009.
12. T. Kailath, A. H. Sayed, and B. Hassibi, Linear Estimation. Upper Saddle River, NJ: Prentice-Hall, 2000.
13. T. Kailath, Linear System. Englewood Clifffs, NJ: Prentice-Hall, 1980.
14. L. Ljung, System Identification: Theory for the User. Englewood Cliffs, NJ: Prentice-Hall, 1999.
15. E. W. Kamen and J. K. Su, Introduction to Optimal Estimation. Lomdon, U.K.: Spinger-Verlag, 1999.
16. H. Dai, Matrix Theory. Beijing, China: Science Press, 2001.