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United States Army Target Acquisition Model Improvement Program

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Works: 3 works in 3 publications in 1 language and 1 library holdings
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Publications about United States
Most widely held works about United States
    A Mathematical Framework for an Improved Search Model by Jeffrey F Nicoll( Book )
    1 edition published in 1994 in English and held by 1 library worldwide
    Search is currently modeled for DoD applications by a single exponential function. The two adjustable parameters are the time constant, t, characterizing the exponential; and the long time detection probability, P. Deficiencies of the classical model are: human performance data cannot typically be fit with a single exponent model; the probability of detection for short times is less than that predicted by the classical model; the effects of multiple targets and clutter can only be included by adjusting the two-model parameters, which is performed in an ad hoc manner and over-constrains the model. This paper introduces a neoclassical model that includes three processes: attending to the target, random wandering around the scene and attending to other targets/clutter. An expression involving three exponents associated with the three processes is derived and special cases are described. The new model provides uniform treatment of multiple targets and false detections and allows for the separate descriptions of multiple times scales within the search process. Searches can be separated into single region, field-of-view search, and multiple region, field-of-regard search. Field-of-view search can be further subdivided into long searches during which the observer may examine many targets and short searches which are completed after a few target examinations
    Statistical and Modeling Uncertainties in the Thermal Target Acquisition Model Improvement Program (TAMIP) Predictions by James D Silk( Book )
    1 edition published in 1995 in English and held by 0 libraries worldwide
    We analyze the uncenainties that are associated with the Thermal Target Acquisition Model Improvement Program (TAMIP) target detection predictions. The deviations of the measured probabilities from the predicted ones are much smaller than for the previous model, but still exceed those that would be expected from the finite statistical samples. Therefore there is still some residual error in the prediction. We determine confidence limits on the predictor variable which are unbiased in the sense that they are accurate at both high and low detection probabilities. We validate the confidence interval using a second data set that was not used in the development of this phase of the target detection model
    A Model of False Alarms in Target Acquisition by Human Observers by James D Silk( Book )
    1 edition published in 1995 in English and held by 0 libraries worldwide
    The modeling of target acquisition by human observers has focused on the problem of predicting whether targets will be detected. The closely related issue of false alarm prediction has received less attention. While predicting false alarms is secondary to true detection, it is nevertheless important to understand the effects of false alarms and to account for them in the development of doctrine. In this work we extend the scope of target acquisition modeling to the consideration of false detections. The model is based on the analysis of data obtained in a series of target acquisition tests. It is phenomenological in the sense that it seeks only to describe the results of the tests. An important finding from the analysis of the test data is that the dominant determinant of false alarm rate is the expectation of the human subject. A more general review of the test results reveals features that strongly suggest a description based on signal detection theory. Re-analysis of the test data in this context allows us to construct such a description and to extract the parameters that describe the observer ensemble. Finally, we demonstrate the correlation between the mean false alarm rate and a scene complexity statistic
 
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English (3)
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