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Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the detection machine and of the nervous system of the operator).
Detection theory, or signal detection theory, is a means to quantify the ability to discern between signal and noise. The main article for this category is detection theory . Subcategories
Detection occurs when the cell under test exceeds the threshold. In most simple CFAR detection schemes, the threshold level is calculated by estimating the noise floor level around the cell under test (CUT). This can be found by taking a block of cells around the CUT and calculating the average power level.
The sensitivity index or d′ (pronounced "dee-prime") is a statistic used in signal detection theory. It provides the separation between the means of the signal and the noise distributions, compared against the standard deviation of the noise distribution.
Matched filters are often used in signal detection. [1] As an example, suppose that we wish to judge the distance of an object by reflecting a signal off it. We may choose to transmit a pure-tone sinusoid at 1 Hz. We assume that our received signal is an attenuated and phase-shifted form of the transmitted signal with added noise.
The sensitivity index or discriminability index or detectability index is a dimensionless statistic used in signal detection theory. A higher index indicates that the signal can be more readily detected.
Green and Swets [10] formulated the Signal Detection Theory, or SDT, in 1966 to characterize detection task performance sensitivity while accounting for both the observer's perceptual ability and willingness to respond. SDT assumes an active observer making perceptual judgments as conditions of uncertainty vary.
The goal of the wiener filter is to compute a statistical estimate of an unknown signal using a related signal as an input and filtering it to produce the estimate. For example, the known signal might consist of an unknown signal of interest that has been corrupted by additive noise. The Wiener filter can be used to filter out the noise from ...