Recursive triangular array ladder algorithms

Two recursive-least-squares ladder algorithms for implementation on triangular systolic arrays are presented. The first algorithm computes transversal forward/backward predictor coefficients, ladder reflection coefficients, and forward/backward residual energies. This algorithm has a complexity of three multiplications and additions per rotational (triangular array) element. A second algorithm is presented that facilitates the computation of only the ladder reflection coefficients and the forward/backward residual energies at a cost of two multiplications and additions per rotational element. This way, both algorithms are computationally more efficient than the traditional recursive QR decomposition (Gentleman and Kung array) for any order. The second algorithm is more efficient than Cioffi's pipelineable linear array fast QR adaptive filter for an order of less than 22 in the prewindowed case, and more efficient than the fast QR for an order of less than 43 in the more general covariance case. A comparison of the presented algorithms and the prominent QR methods is given. The algorithms remain unchanged and the number of arithmetic operations is not increased when finite duration windows are used. The algorithms are based entirely on numerically stable and robust covariance recursions. >

[1]  Tariq S. Durrani,et al.  A triangular adaptive lattice filter for spatial signal processing , 1983, ICASSP.

[2]  John M. Cioffi,et al.  High-speed systolic implementation of fast QR adaptive filters , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[3]  Thomas P. Barnwell,et al.  Recursive windowing for generating autocorrelation coefficients for LPC analysis , 1981 .

[4]  J. L. Roux,et al.  A fixed point computation of partial correlation coefficients , 1977 .

[5]  Peter Strobach,et al.  Linear Prediction Theory: A Mathematical Basis for Adaptive Systems , 1990 .

[6]  S. Kung,et al.  VLSI Array processors , 1985, IEEE ASSP Magazine.

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

[8]  P. Strobach Efficient covariance ladder algorithms for finite arithmetic applications , 1987 .

[9]  O. Macchi,et al.  Comparison of RLS and LMS algorithms for tracking a chirped signal , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[10]  James Durbin,et al.  The fitting of time series models , 1960 .

[11]  J. G. McWhirter,et al.  Recursive Least-Squares Minimization Using A Systolic Array , 1983, Optics & Photonics.

[12]  H. T. Kung,et al.  Matrix Triangularization By Systolic Arrays , 1982, Optics & Photonics.

[13]  John M. Cioffi,et al.  The fast adaptive ROTOR's RLS algorithm , 1990, IEEE Trans. Acoust. Speech Signal Process..

[14]  N. Wiener The Wiener RMS (Root Mean Square) Error Criterion in Filter Design and Prediction , 1949 .

[15]  M. Morf Fast Algorithms for Multivariable Systems , 1974 .

[16]  Fuyun Ling,et al.  A recursive modified Gram-Schmidt algorithm for least- squares estimation , 1986, IEEE Trans. Acoust. Speech Signal Process..

[17]  W. Givens Numerical Computation of the Characteristic Values of a Real Symmetric Matrix , 1954 .

[18]  Daniel T. L. Lee Canonical ladder form realizations and fast estimation algorithms , 1980 .

[19]  Peter Strobach Pure order recursive least-squares ladder algorithms , 1986, IEEE Trans. Acoust. Speech Signal Process..

[20]  N. Levinson The Wiener (Root Mean Square) Error Criterion in Filter Design and Prediction , 1946 .

[21]  W. E. Gentleman Least Squares Computations by Givens Transformations Without Square Roots , 1973 .

[22]  U. Appel,et al.  Recursive lattice algorithms with finite-duration windows , 1982, ICASSP.

[23]  G. Yule On the Theory of Correlation for any Number of Variables, Treated by a New System of Notation , 1907 .

[24]  J. Schur,et al.  Über Potenzreihen, die im Innern des Einheitskreises beschränkt sind. , 1917 .

[25]  J. L. Hock,et al.  An exact recursion for the composite nearest‐neighbor degeneracy for a 2×N lattice space , 1984 .

[26]  H. T. Kung Why systolic architectures? , 1982, Computer.

[27]  Peter Strobach,et al.  Recursive covariance ladder algorithms for ARMA system identification , 1988, IEEE Trans. Acoust. Speech Signal Process..