Stability of Kalman filtering with Markovian packet losses

Automatica

Volumn 43, Issue 4, 2007, Pages 598-607

  Huang, Minyi ^a   Dey, Subhrakanti ^b  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Kalman filtering; Networked systems; Packet losses; Stability

Indexed keywords

ERROR ANALYSIS; MARKOV PROCESSES; SIGNAL FILTERING AND PREDICTION; SYSTEM STABILITY;

COVARIANCE MATRIX; NETWORKED SYSTEMS; PACKET LOSSES; PEAK COVARIANCE;

KALMAN FILTERS;

EID: 33847417432     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2006.10.023     Document Type: Article

References (21)

1. Anderson B.D.O., and Moore J.B. Optimal filtering (1979), Prentice-Hall, Englewood Cliffs, NJ
2. Chong C.-Y., and Kumar S.P. Sensor networks: Evolution, opportunities, and challenges. Proceedings of IEEE 91 (2003) 1247-1256
3. Elliott E.O. Estimation of error rates for codes on burst-noise channels. Bell System Technical Journal 42 (1963) 1977-1997
4. Fletcher, A. K., Rangan, S., & Goyal, V. K. (2004). Estimation from lossy sensor data: Jump linear modeling and Kalman filtering. In Proceedings of the third ACM/IEEE international symposium on information processing in sensor networks (pp. 251-258). Berkeley, CA.
5. Freedman D. Markov chains (1983), Springer, New York
6. Gilbert E.N. Capacity of burst-noise channels. Bell System Technical Journal 39 (1960) 1253-1265
7. Gupta, V., Spanos, D., Hassibi, B., & Murray, R. M. (2005). Optimal LQG control across packet-dropping links. In Proceedings of American control conference (pp. 360-365). Portland, OR.
8. Hadidi M.T., and Schwartz S.C. Linear recursive state estimation under uncertain observations. IEEE Transactions on Automatic Control 24 (1979) 944-948
9. Hadjicostis, C. N., & Touri, R. (2002). Feedback control utilizing packet dropping network links. In Proceedings of IEEE conference on decision and control (pp. 1205-1210). Las Vegas, NV.
10. Jaffer A.G., and Gupta S.C. Recursive Bayesian estimation with uncertain observation. IEEE Transactions on Information Theory 17 (1971) 614-616
11. Kailath T., Sayed A.H., and Hassibi B. Linear estimation (2000), Prentice-Hall, Upper Saddle River, NJ
12. Kalman R.E. A new approach to linear filtering and prediction problems. Trans. ASME-Journal of Basic Engineering Series D 82 (1960) 35-45
13. Ling, Q., & Lemmon, M. (2003). Soft real-time scheduling of networked control systems with dropouts governed by a Markov chain (pp. 4845-4550). Denver, CO.
14. Liu, X., & Goldsmith, A. J. (2004). Kalman filtering with partial observation losses. In Proceedings of IEEE conference on decision control (pp. 1413-1418). Paradise Island, Bahamas.
15. Nahi N.E. Optimal recursive estimation with uncertain observation. IEEE Transactions on Information Theory 15 (1969) 457-462
16. Sinopoli B., Schenato L., Franceschetti M., Poolla K., Jordan M.I., and Sastry S.S. Kalman filtering with intermittent observations. IEEE Transactions on Automatic Control 49 (2004) 1453-1464
17. Smith S.C., and Seiler P. Estimation with lossy measurements: Jump estimators for jump systems. IEEE Transactions on Automatic Control 48 (2003) 2163-2171
18. Tugnait J.K. Asymptotic stability of the MMSE linear filter for systems with uncertain observations. IEEE Transactions on Information Theory 27 (1981) 247-250
19. Varaiya P. Smart cars on smart roads: Problems of control. IEEE Transactions on Automatic Control 38 (1993) 195-207
20. Zhang, H., Moura, J. M. F., & Krogh, B. (2005). Estimation in sensor networks: A graph approach. In Proceedings of the fourth international symposium on information processing in sensor networks (IPSN) (pp. 203-209). Los Angeles.
21. Zhao F., Shin J., and Reich J. Information-driven dynamic sensor collaboration. IEEE Signal Processing Magazine 19 (2002) 61-72