In this paper the Generalized Likelihood Ratio (GLR) method for maneuver detection and estimation is applied to a generic surface-to-air engagement. In the case at hand, the technique results in system model nonlinear...
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In this paper the Generalized Likelihood Ratio (GLR) method for maneuver detection and estimation is applied to a generic surface-to-air engagement. In the case at hand, the technique results in system model nonlinearities which are incorporated in the algorithm to yield an "extended GLR." By employing Monte Carlo simulations, the detection and maneuver estimation performance of the GLR algorithm for various missile-target planar geometries, is evaluated. It is shown that even for a limited information base, maneuver detection and estimation is possible for almost all geometric scenarios.
The first part of this paper has been devoted to the presentation of a sequential edge detection algorithm, the local detector of which uses sequential estimators of change in mean grey level. A detailed study of some...
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The first part of this paper has been devoted to the presentation of a sequential edge detection algorithm, the local detector of which uses sequential estimators of change in mean grey level. A detailed study of some sequential detectors of change in mean is presented in this second part; this study includes some comparisons issued from simulations and new theoretical results concerning the best of them.
A sequential algorithm for edge detection using a line-byline detector of edge elements connected to a recursive edge-following scheme is presented. On each line, edge elements are detected by means of a filtering ope...
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A sequential algorithm for edge detection using a line-byline detector of edge elements connected to a recursive edge-following scheme is presented. On each line, edge elements are detected by means of a filtering operation in order to follow the slow variations of the gray level and some sequential and recursive estimators for locating jumps in this level. The edge-following problem is solved by a Kalman filter, the state model corresponding to a noisy straight line. In this first part, the complete edge detection algorithm is presented after a brief survey of edge detection methods available in the literature. Two main examples of applications are given: detection of white and black targets in the landscape in order to perform automatic driving of vehicles and detection of blood vessels in stereographic images of the brain. In the second part, a detailed study of the sequential estimators for change in mean, which are used in the line-by-line detection, will be found.
Fifty years ago Berger made the first registrations of the electrical activity of the brain with electrodes placed on the intact skull. It immediately became clear that the frequency content of recorded signals plays ...
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Fifty years ago Berger made the first registrations of the electrical activity of the brain with electrodes placed on the intact skull. It immediately became clear that the frequency content of recorded signals plays an important role in describing these signals and also the state of the brain. This paper briefly surveys the main properties the electroencephalogram (EEG), and points out several influential factors. A number of methods have been developed to quantify the EEG in order to complement visual screening; these are conveniently classified as being parametric or nonparametric. The paper emphasizes parametric methods, in which signal analysis is based on a mathematical model of the observed process. The scalar or multivariate model is typically linear, with parameters being either time invariant or time variable. algorithms to fit the model to observed data are surveyed. Results from the analysis my be used to describe the spectral properties of the EEG, including the way in which characteristic variables change with time. Parametric models have successfully been applied to detect the occurrence of transients with epiliptic origin, so-called spikes and sharp waves. Interesting results have also been obtained by combining parameter estimation with classification algorithms in order to recognize significant functional states of the brain. The paper emphasizes methodology but includes also brief accounts of applications for research and clinical use. These mainly serve to illustrate the progress being made and to indicate the need for further work. The rapid advance of computer technology makes the processed EEG an increasingly viable tool in research and clinical practice.
Given a ring (cycle) of n processes it is required to design the processes so that they will be able to choose a leader (a uniquely designated process) by sending messages along the ring. If the processes are indistig...
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Given a ring (cycle) of n processes it is required to design the processes so that they will be able to choose a leader (a uniquely designated process) by sending messages along the ring. If the processes are indistiguishable there is no deterministic algorithm, and therefore probabilistic algorithms are proposed. These algorithms need not terminate, but their expected complexity (time or number of bits of communication) is bounded by a function of n. If the processes work asynchronously then on the average O(n log2n) bits are transmitted. In the above cases the size n of the ring was assumed to be known. If n is not known it is suggested first to determine the value of n and then use the above algorithm. However, n may only be determined probabilistically and any algorithm may yield an incorrect value. In addition, it is shown that the size of the ring cannot be calculated by any probabilistic algorithm in which the processes can sense termination.
In this paper, we present a recursive generalized likelihood ratio (GLR) test algorithm for detecting sudden changes in linear discrete systems. We demonstrate the application of linear filtering techniques to obtain ...
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In this paper, we present a recursive generalized likelihood ratio (GLR) test algorithm for detecting sudden changes in linear discrete systems. We demonstrate the application of linear filtering techniques to obtain a recursive GLR algorithm so that the requirement for matrix inversions in the previously known GLR algorithms can be reduced or avoided. Furthermore, the GLR algorithm is extended to the case when the sudden change follows known linear dynamics. An adaptive filtering scheme which uses the input estimate to correct the state estimate is also presented for the time-varying input case.
A method for text-independent speaker identification has been developed which utilizes vowel sounds as the basis for extracting speaker characteristics. Using 63 minutes of conversational speech data from 20 speakers,...
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A method for text-independent speaker identification has been developed which utilizes vowel sounds as the basis for extracting speaker characteristics. Using 63 minutes of conversational speech data from 20 speakers, it was found that vowel recognition is not necessary. Instead, the vowel samples can be pooled such that they represent each person's vowel space, which is expected to be very speaker-dependent. A sequential analysis process has improved the decision procedure by allowing vowel samples to be tested until a specified level of confidence is reached in the identification. This dynamic decision procedure is similar to a human perception process where we can quickly identify a unique voice, but listen longer when there is uncertainty.
Experimental studies and control descriptions of the human operator in recent years have extended the knowledge of adaptive behaviour in manual control from vague generalities about his versatility to quantitative des...
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Experimental studies and control descriptions of the human operator in recent years have extended the knowledge of adaptive behaviour in manual control from vague generalities about his versatility to quantitative descriptions. Considerable data have been gathered on the ability of the human to adapt to changes in input spectra, controlled element gain, polarity and dynamics, display modality, and the limits of controllability under a variety of situations. These results have indicated a greater adaptation versatility than expected from some earlier descriptions, but have also pointed out some of the restricted training conditions under which the rapid human adaptation may be expected to be demonstrated. When it comes to explaining how the human manages his remarkable adaptation abilities, less progress has been made. Improvements in the techniques for dynamic measurement of the adaptive process have been helpful in this regard but still fall short of what is required to observe the change in control law. Some appealing suggestions for detection models, model reference conitrol analogues and statistical decision theory pattern recognition algorithms have been put forth but are in no sense 'proven'. Primarily as a result of the measurement limitation and the lack of sufficient knowledge about adaptive control systems in general, the development of models for human adaptive control has been limited to general schema at this time.
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