Abstract    A step-by-step algorithm for enhancement of periodic signals that are highly corrupted by additive uncorrelated white gausian noise is proposed. In each adaptation step a new parallel second-order section is added to the previous filters. Every section has only one adjustable parameter, i.e., the center frequency of the self-tuning filter. The bandwidth and the convergence factor of each section is adjusted nonadaptively by a deterministic simple method which results in a stable and accurate regulation of the adaptive parameters. The step-by-step detection of sinusoidal signals prevents the convergence difficulties encountered in adaptive parallel IIR filters. Computer simulation results are presented to show the noise canceling performance of the proposed algorithm. Some comparisons with a new adaptive lattice notch filter for detection of multiple sinusoids are also provided.

Keywords    Adaptive Filters, IIR Filters, Line Enhancement, Parallel Structures

References    1. Widrow, B. and Stearns, S. D. "Adaptive Signal Processing," prentice-Hall, Englewood Cliffs, N. J., (1985). 2. Hush, D. R., “An Adaptive IIR Structure for Sinusoidal Enhancement, Frequency Estimation, and Detection”; IEEE Trans. Acoust, Speech, Sig. Proc., Vol. ASSP-34, No. 6, (Dec. 1986), 1380-1389. 3. Johnson, C. R., Jr. Adaptive IIR filtering, "Current results and open issues"; IEEE Trans. Information theory” Vol. IT-30, No. 2, (Mar.1984) 237-250. 4. Shynk, J. J. "Adaptive IIR filtering" IEEE ASSP Magazine, Vol. 6, (Apr .1989), 4-21. 5. Shynk, J. J. "Adaptive IIR filtering using Parallel form Realizations“; IEEE Trans. Acoust., Speech, Sig. Proc, Vol. ASSP-37, (Apr. 1989), 519-533. 6. J. J.Shynk "Performance of Alternative Adaptive IIR Filter Realizations"; in Proc. 21st Asilomar Conf. Signals, Systems, Computers, Pacific Grove, California, (Nov.1987), 144-150. 7. Zalnieriunas, A. “Simple Adaptive Recursive Filter for Estimation of Trajectories in Noise”; presented in ISSSE 92, Paris, (Sep-1992) 450-453. 8. Oppenheim, A. V. and Schafer, R. W. Digital Signal Processing. Englewood Cliffs, N. J, Prentice-Hall, (1975). 9. Bhaskar Rao, D. V. and Kung, S. Y. “Adaptive Notch Filtering for the Retrieval of Sinusoids in Noise”; IEEE Trans. Acoust., Speech, Sig. Proc, Vol. ASSP-32, (Aug-1984), 791-802. 10. Ghavami, M. "A New Adaptive Recursive Filter"; IEEE Trans. Circuits Systems, Vol. 40, (Jun-1993), 426-429. 11. Widrow, B., McCool, J. M., Larimore, M. G. and Johnson, C. R., Jr. Stationary and Nonstationary Learning Characteristics of the LMS Adaptive Filter.;Proc. IEEE, Vol. 64, (Aug-1976), 1151-1162. 12. Cho, N. I. and Lee, S. U. “On the AdaptiveLattice Notch Filter for the Detection of Sinusoids”; IEEE Trans. Circuits System, Vol. 40, (July-1993), 405-416. 13. wan, T. K and Martin, K. “Adaptive Detection and Enhancement of Multiple Sinusoids using a Cascade IIR Filter,” IEEE Trans. Circuits System, Vol. 36, (July-1989), 937-947. 14. Ljung, L. and Soderstorm, T., “Theory and Practice of Recursive Identification,” Cambridge, MA., MIT press, (1983).