Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations

IET Signal Processing

Volumn 5, Issue 5, 2011, Pages 471-479

  Jamoos, A ^a   Grivel, E ^b   Shakarneh, N ^a   Abdel Nour, H ^a  

^a AL QUDS UNIVERSITY   (Palestine)

^b UNIVERSITÉ BORDEAUX 1   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIVE WHITE NOISE; COMPARATIVE SIMULATION; COMPUTATIONAL COSTS; ITERATIVE APPROACH; MOBILE FADING CHANNELS; MULTI-CHANNEL; MULTIVARIATE AUTOREGRESSIVE; MULTIVARIATE AUTOREGRESSIVE PROCESS; NOISY OBSERVATIONS; ONLINE METHODS; OPTIMAL FILTER; SIGMA-POINT KALMAN FILTERS; SINGLE-CHANNEL; STATE VECTOR;

COMPUTER SIMULATION; ESTIMATION; FADING (RADIO); FADING CHANNELS; KALMAN FILTERS; OPTIMIZATION; WHITE NOISE;

PARAMETER ESTIMATION;

EID: 80051686910     PISSN: 17519675     EISSN: 17519683     Source Type: Journal    
DOI: 10.1049/iet-spr.2010.0066     Document Type: Article

References (30)

1. Chen, J.H., Cox, R.V., Lin, Y.C., Jayant, N., and Melchner, M.J.: 'A low-delay CELP coder for the CCITT 16 kb/s speech coding standard', IEEE J. Sel. Areas Commun., 1992, 10, p. 830-84910.1109/49.138988 0733-8716
2. Guler, I., Kiymik, M.K., Akin, M., and Alkan, A.: 'AR spectral analysis of EEG signals by using maximum likelihood estimation', Comput. Biol. Med., 2001, 31, p. 441-45010.1016/S0010-4825(01)00022-1 0010-4825
3. Baddour, K.E., and Beaulieu, N.C.: 'Autoregressive modeling for fading channel simulation', IEEE Trans. Wirel. Commun., 2005, 4, p. 1650-166210.1109/TWC.2005.850327 1536-1276
4. Wensink, H.: 'On parametric detection of small targets in sea clutter', Proc. Third Int. Conf. Information Fusion, Paris, France, July 10-13 2000, p. 17-24
5. Zheng, W.X.: 'Fast identification of autoregressive signals from noisy observations', IEEE Trans. Circuits Syst.-II: Express Briefs, 2005, 52, p. 43-4810.1109/TCSII.2004.838435
6. Davila, C.E.: 'A subspace approach to estimation of autoregressive parameters from noisy measurements', IEEE Trans. Signal Process., 1998, 46, p. 531-53410.1109/78.655442 1053-587X
7. Mahmoudi, A., and Karimi, M.: 'Parameter estimation of autoregressive signals from observations corrupted with colored noise', Signal Process., 2010, 90, p. 157-16410.1016/j.sigpro.2009.06.005 0165-1684
8. Bobillet, W., Diversi, R., Grivel, E., Guidorzi, R., Najim, M., and Soverini, U.: 'Speech enhancement combining optimal smoothing and errors-in-variables identification of noisy AR processes', IEEE Trans. Signal Process., 2007, 55, p. 5564-557810.1109/TSP.2007.898787 1053-587X
9. Haykin, S.: 'Kalman filtering and neural networks', (John Wiley & Sons, Inc., NY, USA 2001)
10. Labarre, D., Grivel, E., Berthoumieu, Y., Todini, E., and Najim, M.: 'Consistent estimation of autoregressive parameters from noisy observations based on two interacting Kalman filters', Signal Process., 2006, 86, p. 2863-287610.1016/j.sigpro.2005.12.001 0165-1684
11. Labarre, D., Grivel, E., Najim, M., and Christov, N.: 'Dual H∞ algorithms for signal processing - application to speech enhancement', IEEE Trans. Signal Process., 2007, 55, p. 5195-520810.1109/TSP.2007.899587 1053-587X
12. Jamoos, A., Grivel, E., Christov, N., and Najim, M.: 'Estimation of autoregressive fading channels based on two cross-coupled H∞ filters', Signal, Image Video Process. J. (SIViP), Springer, 2009, 3, (4), p. 209-21610.1007/s11760-008-0096-x
13. Arnold, M., Milner, W.H.R., Witte, H., Bauer, R., and Braun, C.: 'Adaptive AR modeling of nonstationary time series by means of Kalman filtering', IEEE Trans. Biomed. Eng., 1998, 45, p. 553-56210.1109/10.668741 0018-9294
14. Korhonen, I., Takalo, R., and Turjanmaa, V.: 'Multivariate autoregressive model with immediate transfer paths for assessment of interactions between cardiopulmonary variability signals', Med. Biol. Eng. Comput., 1996, 34, p. 199-20610.1007/BF02520074 0140-0118
15. Russ, J., Casbeer, D., and Swindlehurst, A.L.: 'STAP detection using space-time autoregressive filtering', Proc. IEEE-Radar Conf., Philadelphia, USA 2004, p. 541-545
16. Abramovich, Y.I., Johnson, B.A., and Spencer, N.K.: 'Two-dimensional multivariate parametric models for radar applications-part i: Maximum-entropy extensions for toeplitz-block matrices', IEEE Trans. Signal Process., 2008, 56, p. 5509-552610.1109/TSP.2008.929868 1053-587X
17. Chen, W., and Zhang, R.: 'Estimation of time and frequency selective channels in OFDM systems: a Kalman filter structure', Proc. IEEE-Globecom, TX, USA 2004, p. 800-803
18. Komninakis, C., Fragouli, C., Sayed, A.H., and Wesel, R.D.: 'Multi-input multi-output fading channel tracking and equalization using Kalman estimation', IEEE Trans. Signal Process., 2002, 50, p. 1065-107610.1109/78.995063 1053-587X
19. Gao, W., Tsai, S., and Lehnert, J.S.: 'Diversity combining for DS/SS systems with time-varying, correlated fading branches', IEEE Trans. Commun., 2003, 51, p. 284-29510.1109/TCOMM.2003.809275 0090-6778
20. Schlögl, A.: 'A comparaison of multivariate AR estimators', Signal Process., 2006, 86, p. 2426-242910.1016/j.sigpro.2005.11.007 0165-1684
21. Marple, S.L., and Nuttall, A.H.: 'Experimental comparison of three multichannel linear prediction spectral estimators', IEE Proceedings-F, 1983, 130, p. 218-229
22. Neumaier, A., and Schneider, T.: 'Estimation of parameters and eigenmodes of multivariate autoregressive models', ACM Trans. Math. Softw., 2001, 27, p. 27-5710.1145/382043.382304 0098-3500
23. Hasan, K., Hossain, J., and Haque, A.: 'Parameter estimation of multichannel autoregressive processes in noise', Signal Process., 2003, 83, p. 603-61010.1016/S0165-1684(02)00491-7 0165-1684
24. Mahmoudi, A., and Karimi, M.: 'Estimation of the parameters of multichannel autoregressive signals from noisy observations', Signal Process., 2008, 88, p. 2777-278310.1016/j.sigpro.2008.06.004 0165-1684
25. Petitjean, J., Grivel, E., Bobillet, W., and Roussilhe, P.: 'Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue', Signal Image Video Process. J. (SIViP), Springer, 2010, 4, (2), p. 209-22010.1007/s11760-009-0112-9
26. Cai, J., Shen, X., and Mark, J.W.: 'Robust channel estimation for OFDM wireless communication systems - an H∞ approach', IEEE Trans. Wirel. Commun., 2004, 3, p. 2060-207110.1109/TWC.2004.837295 1536-1276
27. Shen, X., and Deng, L.: 'Game theory approach to discrete H∞ filter design', IEEE Trans. Signal Process., 1997, 45, p. 1092-109510.1109/78.564201 1053-587X
28. Shen, X., and Deng, L.: 'A dynamic system approach to speech enhancement using the H∞ filtering algorithm', IEEE Trans. Speech Audio Process., 1999, 7, p. 391-39910.1109/89.771261 1063-6676
29. Yaesh, I., and Shaked, U.: 'Game theory approach to optimal linear state estimation and its relation to the minimum H∞ - norm estimation', IEEE Trans. Autom. Control, 1992, 37, p. 828-83110.1109/9.256343 0018-9286
30. Baddour, K.E., and Beaulieu, N.C.: 'Accurate simulation of multiple cross-correlated rician fading channels', IEEE Trans. Commun., 2004, 52, p. 1650-166210.1109/TCOMM.2004.836559 0090-6778