Analysis of an adaptive technique for modeling sparse systems

An effective technique for modeling sparse systems has been developed, and the error surface of the system is analyzed with respect to estimation noise. The technique requires a type of adaptive filter which is called an adaptive delay filter. An implementation of the adaptive delay filter is discussed that includes adaptive gains in addition to variable delay taps. The filter is especially applicable to modeling systems with a sparse impulse response. Less computation is required for a sparse system than with the conventional approach. The technique is tested with a variety of unknown systems using both white noise input and autoregressive input. It is shown that it works properly for both sparse and nonsparse systems in noise-free and noisy conditions. The performance of the technique is verified by a careful analysis of the error surface and the techniques for delay determination and corresponding gain adaption. >

[1]  Alan V. Oppenheim,et al.  Applications of digital signal processing , 1978 .

[2]  L. Griffiths.  A continuously-adaptive filter implemented as a lattice structure , 1977 .

[3]  G.L. Turin,et al.  Introduction to spread-spectrum antimultipath techniques and their application to urban digital radio , 1980, Proceedings of the IEEE.

[4]  D. M. Etter,et al.  A Parallel Implementation of the Adaptive Delay Filter , 1985, Nineteeth Asilomar Conference on Circuits, Systems and Computers, 1985..

[5]  D. Etter,et al.  Identification of sparse impulse response systems using an adaptive delay filter , 1985, ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[6]  Michael G. Larimore,et al.  Thinned impulse responses for adaptive FIR filters , 1982, ICASSP.

[7]  Jont B. Allen,et al.  FIR system modeling and identification in the presence of noise and with band-limited inputs , 1978 .

[8]  D. Etter,et al.  Adaptive estimation of time delays in sampled data systems , 1981 .

[9]  Lloyd J. Griffiths,et al.  An adaptive lattice structure for noise-cancelling applications , 1978, ICASSP.

[10]  Bishnu S. Atal,et al.  A new model of LPC excitation for producing natural-sounding speech at low bit rates , 1982, ICASSP.

[11]  D. Etter,et al.  An adaptive delay filter with varibale gains and variable taps , 1986, ICASSP '86. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  D. M. Etter.  Adaptive Delay Tracking with a Varying SNR , 1984, 1984 American Control Conference.

[13]  R. Lynn Kirlin,et al.  Seismic velocity estimators , 1983, ICASSP.

[14]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[15]  D. Etter,et al.  An adaptive technique for multiple echo cancelation in telephone networks , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[16]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

[17]  D. M. Etter,et al.  Time delay estimation using the adaptive delay element , 1983 .

[18]  Don R. Hush,et al.  A new technique for adaptive frequency estimation and tracking , 1987, IEEE Trans. Acoust. Speech Signal Process..

[19]  K. Senne,et al.  Performance advantage of complex LMS for controlling narrow-band adaptive arrays , 1981 .

[20]  E. Robinson,et al.  The impact of refraction on time-delay estimation , 1983, ICASSP.

[21]  Delores M. Etter,et al.  System modeling using an adaptive delay filter , 1987 .

[22]  Tien C. Hsia.  Convergence analysis of LMS and NLMS adaptive algorithms , 1983, ICASSP.

[23]  W. Burdic.  Underwater Acoustic System Analysis , 1984 .

[24]  N. Bershad,et al.  The complex LMS adaptive algorithm--Transient weight mean and covariance with applications to the ALE , 1983 .