NASA Scientific and Technical Aerospace Reports

shift-invariant property. Thus, computation and implementation requirements are reduced by an order of magnitude. The new
algorithms are based on a structure that is neither a transversal filter nor a lattice, but can be best characterized by a
functionally equivalent set of parameters that represent the time-varying ‘least-squares frequency transforms’ of the input
sequences. Numerical stability can be insured by implementing computations as 2x2orthogonal (Givens) rotations.
Author
Adaptive Filters; Least Squares Method; Frequency Domain Analysis; Algorithms
20060001680 Notre Dame Univ., IN, USA
The Use of Large Adaptive Gains to Remove the SPR Condition from Recursive Adaptive Algorithms
Tang, Kun; Rohrs, Charles E.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 4.8.1-4.8.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
Adaptive recursive algorithms using output error have some very attractive properties for both adaptive recursive filtering
and system identification. However, their use has been severely restricted by the need to satisfy the so-called SPR condition.
In this paper we demonstrate on the well known HARF algorithm that the use of large gains can essentially eliminate the need
for the SPR condition. This should allow for more widespread use of adaptive recursive filtering and output error system
identification.
Author
Adaptive Filters; Algorithms; System Identification
20060001693 Northeastern Univ., Boston, MA, USA
Adaptive Transversal Filters with Delayed Coefficient Adaptation
Long, Guozhu; Ling, Fuyun; Proakis, John G.; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 11.10.1-11.10.4; In English; See also 20060001583
Contract(s)/Grant(s): 8507430; Copyright; Avail.: Other Sources
In practical applications of the LMS adaptive transversal filtering algorithm, a delay in the coefficient update is sometimes
required. In this paper, the behavior of the Delayed LMS (DLMS) algorithm is studied. A stablility bound for the step size
is derived. The relation between the step size and the convergence speed and the effect of the delay on the convergence
behavior are also investigated. Computer simulation results are provided and compared wit the analytical results.
Author
Adaptive Filters; Least Squares Method; Algorithms; Delay
20060001702 North Carolina State Univ., Raleigh, NC, USA
Numerical Properties of a Hyperbolic Rotation Method for Windowed RLS Filtering
Alexander, S. T.; Pan, C. T.; Plemmons, R. J.; IEEE International Conference on Acoustics, Speech, and Signal Processing
(ICASSP ‘87); Volume 1; 1987, pp. 11.8.1-11.8.4; In English; See also 20060001583
Contract(s)/Grant(s): DCI-8552571; DMS-85-21154; AFOSR-83-0255-C; Copyright; Avail.: Other Sources
Numerical properties of the hyperbolic rotation method for windowed RLS filtering are examined. This matrix-oriented
approach is important from two standpoints: (1) it provides the LS predictor for a sliding window block of data, and (2) it is
amenable to parallel implementation. It is shown how a hyperbolic rotation matrix may be constructed to update the LS
Cholesky factor as a function of the previous Cholesky factor and the data in the sliding window. Finally, it is shown that the
hyperbolic rotation method is stable for observation matrices which are not rank-deficient.
Author
Rotation; Hyperbolic Functions; Algorithms; Numerical Analysis; Least Squares Method
20060001706 Georgia Inst. of Tech., Atlanta, GA, USA
Differential Operator Based Edge and Line Detection
Bevington, J. E.; Mersereau, R. M.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP
‘87); Volume 1; 1987; In English; See also 20060001583
Contract(s)/Grant(s): DAAG29-84-K-0024; Copyright; Avail.: Other Sources
Edge detection in sampled images may be viewed as a problem of numerical differentiation. In fact, most point edge
operators function by estimating the local gradient or Laplacian. Adopting this view, Torre and Poggio [2] apply regularization
techniques to the problem of computing derivatives, and arrive at a class of simple linear estimators involving derivatives of
a low-pass Gaussian kernel. In this work, we further develop the approach by examining statistical properties of such
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