Steady state analysis of the CLMS and augmented CLMS algorithms for noncircular complex signals

Conference Record - Asilomar Conference on Signals, Systems and Computers

Volumn , Issue , 2010, Pages 1635-1639

  Mandic, Danilo P ^a   Xia, Yili ^a   Douglas, Scott C ^b  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b SOUTHERN METHODIST UNIVERSITY   (United States)

Author keywords

augmented CLMS (ACLMS); augmented complex statistics; convergence; energy conservation principle; improperness; steady state analysis; Widely linear modelling

Indexed keywords

AUGMENTED CLMS (ACLMS); AUGMENTED COMPLEX STATISTICS; CONVERGENCE; ENERGY CONSERVATION PRINCIPLE; IMPROPERNESS; STEADY-STATE ANALYSIS; WIDELY LINEAR;

ALGORITHMS; CONVERGENCE OF NUMERICAL METHODS; ENERGY CONSERVATION; ERROR STATISTICS; MEAN SQUARE ERROR;

PROBABILITY DISTRIBUTIONS;

EID: 79957982382     PISSN: 10586393     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACSSC.2010.5757815     Document Type: Conference Paper

References (15)

1. A. H. Sayed, Adaptive Filters, John Wiley & Sons, 2008.
2. P. J. Schreier and L. L. Scharf, "Second-order analysis of improper complex random vectors and process," IEEE Trans. Signal Process., vol. 51, no. 3, pp. 714-725, 2003.
3. F. D. Neeser and J. L. Massey, "Proper complex random processes with applications to information theory," IEEE Trans. Inform. Theory, vol. 39, no. 4, pp. 1293-1302, 1993.
4. B. Picinbono and P. Chevalier, "Widely linear estimation with complex data," IEEE Trans. Signal Process., vol. 43, no. 8, pp. 2030-2033, 1995.
5. R. Schober, W. H. Gerstacker, and L. H. J. Lampe, "Date-aided and blind stochastic gradients for widely linear MMSE MAI supression for DS-CDMA," IEEE Trans. Signal Process., vol. 52, no. 3, pp. 746-756, 2004.
6. S. Javidi, S. L. Goh M. Pedzisz, and D. P. Mandic, "The augmented complex least mean square algorithm with application to adaptive prediction problems," Proc. 1st IARP Workshop Cogn. Inform. Process., pp. 54-57, 2008.
7. S. C. Douglas, "Widely linear Recursive Least-Squares algorithm for adaptive beamforming," Proc. IEEE Int. Conf. Acoust., Speech Signal Process., pp. 2041-2044, 2009.
8. Y. Xia, C. Cheong-Took, and D. P. Mandic, "An augmented affine projection algorithm for the filtering of noncircular complex signals," Signal Process., vol. 90, no. 6, pp. 1788-1799, 2010.
9. S. C. Douglas, "Fixed-point algorithms for the blind separation of arbitrary complex-valued non-Gaussian signal mixtures," EURASIP J. Adv. Signal Process., vol. 2007, no. 1, pp. 83-83, 2007.
10. S. Javidi, D. P. Mandic, and A. Cichocki, "Complex blind source extraction from noisy mixtures using second-order statistics," IEEE Trans. Circuits Syst. I, vol. 57, no. 7, pp. 1404-1416, 2010.
11. S. C. Douglas and D. P. Mandic, "Mean and mean-square analysis of the complex LMS algorithm for non-circular Gaussian signals," Proc. IEEE Workshop Digital Signal Process., pp. 101-106, 2009.
12. S. C. Douglas and D. P. Mandic, "Performance analysis of the conventional complex LMS and augmented complex LMS algorithms," Proc. IEEE Int. Conf. Acoust., Speech Signal Process., pp. 3794-3797, 2010.
13. B. Widrow, J. McCool, and M. Ball, "The complex LMS algorithm," Proc. IEEE, vol. 63, pp. 719-720, 1975.
14. D. P. Mandic and S. L. Goh, Complex Valued Nonlinear Adaptive Filters: Noncircularity, Widely Linear and Neural Models, John Wiley & Sons, 2009.
15. T. Y. Al-Naffouri and A. H. Sayed, "Transient analysis of data-normalized adaptive filters," IEEE Trans. Signal Process., vol. 51, no. 3, pp. 639-652, 2003.