NASA Scientific and Technical Aerospace Reports

20040073610 Army Research Lab., Aberdeen Proving Ground, MD
Acquiring Data for the Development of a Finite Element Model of an Airgun Launch Environment
Szymanski, Edward A.; Mar. 2004; 34 pp.; In English; Original contains color illustrations
Report No.(s): AD-A422483; ARL-MR-581; No Copyright; Avail: CASI; A03, Hardcopy
The objective was to obtain both strain data (ue) and acceleration data (g) of a test article to aid in the development of
a finite element model of an airgun launch environment. A 4-in airgun was utilized to obtain the required data from the test
article. The author’s responsibility for these series of airgun tests was to instrument the test article, configure and calibrate the
on-board recorder, and retrieve and process the acquired data from each of the three airgun tests.
DTIC
Data Acquisition; Finite Element Method; Gas Guns; Launching; Mathematical Models
20040073695 Naval Undersea Warfare Center, Newport, RI
Optimum Detection of Random Signal in Non-Gaussian Noise for Low Input Signal-to-Noise Ratio
Nuttall, Albert H.; Feb. 23, 2004; 49 pp.; In English; Original contains color illustrations
Report No.(s): AD-A422595; NUWC-NPT-TR-11; 512; No Copyright; Avail: CASI; A03, Hardcopy
Optimum detection of a weak stationary random signal in independent non-Gaussian noise requires knowledge of the
first-order probability density function of the noise and the covariance function of the signal. More precisely, the first and
second derivatives of the input noise probability density function must be known to realize the optimum processor. When these
two derivatives must be estimated from a finite segment of noise-only data, a severe demand is placed on the amount of
required data. Estimation of higher derivatives of histograms is not accomplished reliably without considerable amounts of
data. The presence of heavy-tailed noise data exacerbates this issue. The situation improves when Gaussian noise is
considered, mainly because the second derivative of the noise density is not relevant or required for Gaussian noise; all other
noise densities must have this information to achieve optimum detection. The samples of the random input signal process need
not be taken at an independent rate. However, the covariance of the signal process must be known for optimum processing.
The joint probability density function of the input signal is not required for low input signal-to-noise ratios. A simple example
of a multipath signal is presented in this report that indicates the need for knowledge of the relative path strengths and the
multipath delay time. Lack of knowledge of these parameters in this model requires multiple guesses at their values and
parallel processors; the amount of degradation depends on the uncertainty of the medium characteristics.
DTIC
Random Noise; Random Signals; Signal Detection; Signal to Noise Ratios
20040073732 Rice Univ., Houston, TX
Wavelet-Based Bayesian Methods for Image Analysis and Automatic Target Recognition
Nowak, Robert D.; Feb. 15, 2001; 6 pp.; In English
Contract(s)/Grant(s): DAAD19-99-1-0349
Report No.(s): AD-A422648; 00072804; ARO-P-40447.2-CI-YIP; No Copyright; Avail: CASI; A02, Hardcopy
This work investigates the use or Bayesian multiscale techniques for image analysis and automatic target recognition. We
have developed two new techniques. First, we have develop a wavelet-based approach to image restoration and deconvolution
problems using Bayesian image models and an alternating-maximation method. Second, we have developed a wavelet-based
framework for target modeling and recognition that we call TEMPLAR (TEMPlate Learning from Atomic Representations)
. TEMPLAR is can he used to automatically extract low-dimensional wavelet representations (or templates) or target objects
from observation data, providing robust and computationally efficient target classifiers. On a more theoretical level, we have
developed a framework for multiresolution analysis or likelihood functions, which extends wavelet-like analysis to a wide
class or non-Gaussian processes. In another line of investigation, we are exploring a new imaging application known as
network tomography. The goal of this work is to characterize the internal performance of communication networks based only
on external measurements at the edge (sources and receivers) of the network. In the coming year, we plan to focus on four
key research areas. First, we will develop theoretical hounds on the performance of multiscale/wavelet estimators in
non-Gaussian environments including Poisson imaging applications. Second, we will study the use of complex wavelets in
image restoration and target recognition problems. Third, we will develop automatic methods for segmenting imagery (SAR,
FLIR, LADAR) based on complexity- regularization methods. Fourth, we will continue to develop a unified framework for
communication network tomography and investigate new tools for network performance visualization.
DTIC
Bayes Theorem; Image Analysis; Image Processing; Restoration; Target Recognition; Templates; Wavelet Analysis
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