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All sourcesNASA NTRSDOE OSTI
NASA NTRS1972

The application of signal detection theory to optics

The role of measurements of noncommuting quantum observables is considered in the detection of signals and estimation of signal parameters by quantum receivers. The restoration of images focused on a photosensitive surface is discussed for data as numbers of photoelectrons ejected from various parts of the surface. The detection of an image formed on a photosensitive surface in the presence of background illumination for similar data is also considered.

Helstrom, C. W.↗
NASA NTRS1991

Tables of square-law signal detection statistics for Hann spectra with 50 percent overlap

The Search for Extraterrestrial Intelligence, currently being planned by NASA, will require that an enormous amount of data be analyzed in real time by special purpose hardware. It is expected that overlapped Hann data windows will play an important role in this analysis. In order to understand the statistical implication of this approach, it has been necessary to compute detection statistics for overlapped Hann spectra. Tables of signal detection statistics are given for false alarm rates from 10(exp -14) to 10(exp -1) and signal detection probabilities from 0.50 to 0.99; the number of computed spectra ranges from 4 to 2000.

Deans, Stanley R.↗
NASA NTRS1972

Geometry of quantum signal detection.

Consideration of a binary quantum signal detection problem in a two-dimensional Hilbert space. The optimum detection problem is reduced to the problem of finding the locus of a vector which has a maximum projection along the fixed a priori probability vector of hypotheses. It is shown that the desired locus can be determined by a geometrical method involving the use of a randomized decision strategy. It is further shown that this geometrical method can be applied to the optimum solution of a binary detection problem described in a product Hilbert space.

Harger, R. O.↗
NASA NTRS1997

Visual signal detection in structured backgrounds. II. Effects of contrast gain control, background variations, and white noise

Studies of visual detection of a signal superimposed on one of two identical backgrounds show performance degradation when the background has high contrast and is similar in spatial frequency and/or orientation to the signal. To account for this finding, models include a contrast gain control mechanism that pools activity across spatial frequency, orientation and space to inhibit (divisively) the response of the receptor sensitive to the signal. In tasks in which the observer has to detect a known signal added to one of M different backgrounds grounds due to added visual noise, the main sources of degradation are the stochastic noise in the image and the suboptimal visual processing. We investigate how these two sources of degradation (contrast gain control and variations in the background) interact in a task in which the signal is embedded in one of M locations in a complex spatially varying background (structured background). We use backgrounds extracted from patient digital medical images. To isolate effects of the fixed deterministic background (the contrast gain control) from the effects of the background variations, we conduct detection experiments with three different background conditions: (1) uniform background, (2) a repeated sample of structured background, and (3) different samples of structured background. Results show that human visual detection degrades from the uniform background condition to the repeated background condition and degrades even further in the different backgrounds condition. These results suggest that both the contrast gain control mechanism and the background random variations degrade human performance in detection of a signal in a complex, spatially varying background. A filter model and added white noise are used to generate estimates of sampling efficiencies, an equivalent internal noise, an equivalent contrast-gain-control-induced noise, and an equivalent noise due to the variations in the structured background.

NASA Discipline Space Human Factors↗
NASA NTRS1977

An asymptotic analysis of a general class of signal detection algorithms

For applications to the problem of radio frequency interference identification, or in the search for extraterrestrial intelligence, it is important to have a basic understanding of signal detection algorithms. A general technique for assessing the asymptotic sensitivity of a broad class of signal detection algorithms is given. In these algorithms, the decision is based on the value of X sub 1 + X sub 2...+ X sub n where the X sub 1's are obtained by sampling and preliminary processing of a physical process.

Mceliece, R. J.↗
NASA NTRS1975

Psychophysical Models for Signal Detection with Time Varying Uncertainty

Psychophysical models for the behavior of the human operator in detection tasks which include change in detectability, correlation between observations and deferred decisions are developed. Classical Signal Detection Theory (SDT) is discussed and its emphasis on the sensory processes is contrasted to decision strategies. The analysis of decision strategies utilizes detection tasks with time varying signal strength. The classical theory is modified to include such tasks and several optimal decision strategies are explored. Two methods of classifying strategies are suggested. The first method is similar to the analysis of ROC curves, while the second is based on the relation between the criterion level (CL) and the detectability. Experiments to verify the analysis of tasks with changes of signal strength are designed. The results show that subjects are aware of changes in detectability and tend to use strategies that involve changes in the CL's.

Gai, E.↗
NASA NTRS1972

Binaural signal detection - Equalization and cancellation theory.

The improvement in masked-signal detection afforded by two ears (i.e., binaural unmasking) is explained on the basis of a descriptive model of the processing of binaural stimuli by a system consisting of two bandpass filters, an equalization and cancellation mechanism, and a decision device. The main ideas of the model are initially explained, and a general equation is derived for the purpose of making quantitative predictions. Comparisons are then made between various special cases of this equation and experimental data. Failures of the preliminary model in predicting the data are considered, and possible revisions are discussed.

Durlach, N. I.↗
NASA NTRS1993

Signal detection theory and methods for evaluating human performance in decision tasks

Signal Detection Theory (SDT) can be used to assess decision making performance in tasks that are not commonly thought of as perceptual. SDT takes into account both the sensitivity and biases in responding when explaining the detection of external events. In the standard SDT tasks, stimuli are selected in order to reveal the sensory capabilities of the observer. SDT can also be used to describe performance when decisions must be made as to the classification of easily and reliably sensed stimuli. Numbers are stimuli that are minimally affected by sensory processing and can belong to meaningful categories that overlap. Multiple studies have shown that the task of categorizing numbers from overlapping normal distributions produces performance predictable by SDT. These findings are particularly interesting in view of the similarity between the task of the categorizing numbers and that of determining the status of a mechanical system based on numerical values that represent sensor readings. Examples of the use of SDT to evaluate performance in decision tasks are reviewed. The methods and assumptions of SDT are shown to be effective in the measurement, evaluation, and prediction of human performance in such tasks.

Obrien, Kevin↗