We present a computational method to determine if an observed time series possesses structure statistically distinguishable from high-dimensional linearly correlated noise, possibly with a nonwhite spectrum. This meth...
We present a computational method to determine if an observed time series possesses structure statistically distinguishable from high-dimensional linearly correlated noise, possibly with a nonwhite spectrum. This method should be useful in identifying deterministic chaos in natural signals with broadband power spectra, and is capable of distinguishing between chaos and a random process that has the same power spectrum. The method compares nonlinear predictability of the given data to an ensemble of random control data sets. A nonparametric statistic is explored that permits a hypothesis testing approach. The algorithm can detect underlying deterministic chaos in a time series contaminated by additive random noise with identical power spectrum at signal to noise ratios as low as 3 dB. With less noise, this method can also be used to get good estimates of the parameters (the embedding dimension and the time delay) needed to perform the standard phase-space reconstruction of a chaotic time series.
作者：
WEBSTER, WPThe Author The Author is the technology manager for the Intercept Weapons Department at the Naval Weapons Center
China Lake Calif. Dr. Webster received his B.S. M.S. and Ph.D. degrees in electrical engineering at the University of Arizona Tucson Arizona in 1964 1966 and 1971 respectively. While at the University of Arizona his studies focused on electromagnetic field propagation and scattering and laser radar system design. Since graduation he has worked at the Naval Weapons Center in a variety of technology areas including laser radar system design infrared system design proximity fuze system design hybrid and monolithic microelectronic circuit design and fabrication technology airborne fire control sensor technology and anti-air and anti-surface missile guidance and control technology. Currently he is responsible for identifying selecting and directing the development of advanced guidance sensor signal processing and aerodynamic control system technologies for application to U.S. Navy tactical electro-optical and radio frequency air intercept missiles. Dr. Webster is a member of IEEE Sigma Xi and Tau Beta Pi.
Complex processing algorithms associated with requirements for real-time target detection, acquisition and recognition have far outdistanced our ability to package the necessary processing power into real-time weapon-...
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Complex processing algorithms associated with requirements for real-time target detection, acquisition and recognition have far outdistanced our ability to package the necessary processing power into real-time weapon-sized hardware. Weapon developers need new, radically different, approaches to solve these difficult weapon signalprocessing problems. Artificial neural networks (ANN) is one of the new, radically different, signalprocessing approaches that is currently receiving much attention for weapon applications. The human brain is a powerful image and pattern recognition processor whose basic processing element is the neuron. Synapses are the weighted interconnections between neurons that permit learning and communication between the neurons. Artificial neural network technology attempts to mathematically and/or electrically model neurons and synapses and then interconnect these models in architectures suitable for signalprocessing tasks. ANN technology is particularly applicable to pattern recognition, speech recognition, machine vision, robotics, and optimization signalprocessing tasks. Specific military applications include missile seekers, missile fuzing, sonar target discrimination, automatic target recognition, and autopilots. Two unique characteristics of ANN processors are that they are non-linear processors and that they are trained, not programmed, to accomplish processing tasks in a manner analogous to the way the human brain learns. Learning is achieved by modifying the synaptic weights of each artificial neuron until the final desired system processing response is achieved. Several years ago the Naval Weapons Center (NavWpnCen) identified ANN technology as a high risk-high payoff approach to missile signalprocessing requirements and initiated several research and development efforts in this area. One result of this work is the new 80170NW ANN analog VLSI chip produced by Intel Inc. Another is the current Missileborne Integrated Neural Network De
The authors describe a knowledge-based system for detecting the interior and exterior boundaries of the left ventricle (LV) from time-varying cross-sectional images of the beating heart obtained noninvasively by magne...
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The authors describe a knowledge-based system for detecting the interior and exterior boundaries of the left ventricle (LV) from time-varying cross-sectional images of the beating heart obtained noninvasively by magnetic resonance imaging (MRI). The system automatically classifies and measures cardiac function through estimates of LV wall thickness, wall motion, etc. The system is knowledge based and it makes use of Dempster-Shafer theory to manage the knowledge. This theory is also used to control the flow of system information for more efficient use of limited computational resources and memory space.< >
An efficient blockwise algorithm, namely the block sequential least-squares (BSLS) algorithm, is presented for sequentially solving LS problems in realtime. The information is carried from block to block by iterating ...
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An efficient blockwise algorithm, namely the block sequential least-squares (BSLS) algorithm, is presented for sequentially solving LS problems in realtime. The information is carried from block to block by iterating some correlation vectors. In the case of successive data blocks, the exactness of the BSLS algorithm is achieved at approximately the same computational requirement as characterizes the nonexact BFTF (block fast transversal filter) algorithm, which is significantly less than sample-by-sample RLS (recursive least squares) algorithms. However, the BSLS cannot accommodate the case of discontinuous blocks of data, which can be accommodated (at the expense of a nonexact solution) by the BFTF. It is shown that the BSLS algorithm allows efficient use of the FFT fast Fourier transform technique to make remarkable gains in computational complexity savings. Additionally, the BSLS algorithm can provide an improved numerical stability over the existing fast RLS algorithms. The numerical performance is illustrated by applications to adaptive equalization and online parameter identification.< >
An efficient blockwise algorithm, named the block sequential least-squares (BSLS) algorithm, is presented for sequentially solving exact LS problems in real time. The information is carried from block to block by iter...
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An efficient blockwise algorithm, named the block sequential least-squares (BSLS) algorithm, is presented for sequentially solving exact LS problems in real time. The information is carried from block to block by iterating some correlation vectors. The average operations of the BSLS are fewer than sample-by-sample fast RLS algorithms. The BSLS algorithm allows efficient use of the FFT technique to make gains in computational complexity savings. It is shown that the new algorithm can provide improved numerical features over the existing fast-RLS algorithms due to the new adaptation version.< >
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