NASA Scientific and Technical Aerospace Reports

We define a piecewise AR model for a class of time series whose statistical properties change abruptly at some unknown
time points. For such a model we consider the problems of jump detection and fast tracking of the changing parameters. A
method based on the adaptive least squares lattice filter algorithm is proposed. The method automatically detects the
occurrences of jumps and adjusts the adaptation rate of the adaptive lattice algorithm, so both accurate estimates and fast
tracking ability are achieved.
Author
Adaptive Filters; Statistical Distributions; Time Series Analysis; Tracking (Position); Detection; Adaptation; Algorithms
20060001735 North Carolina State Univ., Raleigh, NC, USA
An Analysis of Finite Precision Effects for the Autocorrelation Method and Burg’s Method of Linear Prediction
Alexander, S. T.; Rhee, Z. M.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87);
Volume 1; 1987, pp. 9.4.1 - 9.4.4; In English; See also 20060001583
Contract(s)/Grant(s): NSF ECS-84-0655; Copyright; Avail.: Other Sources
New analytical results are derived for quantifying the performance degradation induced by finite precision arithmetic
upon the autocorrelation method and Burg’s algorithm for linear prediction. Analysis shows that reflection coefficients
computed by the FP autocorrelation method have more severe degradation than those computed by the FP Burg algorithm.
Finally, experimental results are presented which show very close agreement between the analytical derivations and
experimental results.
Author
Autocorrelation; Linear Prediction; Algorithms; Precision
20060001737 University of South Florida, Tampa, FL, USA
Autocorrelation Distortion Function for Improved AR Modeling
Jain, V. K.; Xu, B. L.; IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP ‘87); Volume 1;
1987, pp. 9.9.1 - 9.9.4; In English; See also 20060001583; Copyright; Avail.: Other Sources
For noisy signals it is not enough to achieve a match between the first p+1 correlations of the measured data and that of
the AR model. The higher order correlations begin to diverge as the level of data noise increases. In an effort to perform
extended correlation matching we have found the need to use an Autocorrelation Distortion Function (ADF), defined as the
difference between the sample acf of the noisy-data and the statistical acf of the true signal. Using the model ba(sup [n]) for
the ADF we develop a procedure for improved AR modeling of a stationary time series. In this procedure we estimate the
parameters of the ADF in such a way that while the autocorrelation of the signal model plus noise matches the data acf exactly
for the first p+1 lags, simultaneously, the mismatch at certain higher-order lags is minimized. These higher-order lags are taken
to be p+1, .... ,p+q or, alternatively, they are chosen according to a peak-picking scheme described in the paper.
Author
Autocorrelation; Distortion; Time Series Analysis; Autoregressive Processes
20060001771 North Carolina State Univ., Raleigh, NC USA
Optimal Tracking Using Magnetostrictive Actuators Operating in Nonlinear and Hysteretic Regimes
Oates, William S.; Smith, Ralph C.; Jan. 1, 2005; 31 pp.; In English; Original contains color illustrations
Report No.(s): AD-A440159; No Copyright; Avail.: Defense Technical Information Center (DTIC)
many active materials exhibit nonlinearities and hysteresis when driven at field levels necessary to meet stringent
performance criteria in high performance applications. This often requires nonlinear control designs to effectively compensate
for the nonlinear, hysteretic field-coupled material behavior. In this paper, an optimal control design is developed to accurately
track a reference signal using magnetostrictive transducers. The methodology can be directly extended to transducers
employing piezoelectric materials or shape memory alloys (SMAs) due to the unified nature of the constitutive model
employed in the control design. The constitutive model is based on a framework that combines energy analysis at lattice length
scales with stochastic homogenizations techniques to predict macroscopic material behavior. The constitutive model is
incorporated into a finite element representation of the magnetostrictive transducer which provides the framework for
developing the finite-dimensional nonlinear control design. The control design includes an open loop nonlinear component
computed off-line with perturbation feedback around the optimal state trajectory. Estimation of unmeasurable states is
achieved using a Kalman filter. The hybrid control technique provides the potential for real-time control implementation while
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