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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).
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 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.
Definition [ edit ] The discriminability index is the separation between the means of two distributions (typically the signal and the noise distributions), in units of the standard deviation .
In general, detection is the action of accessing information without specific cooperation from with the sender. In the history of radio communications, the term " detector " was first used for a device that detected the simple presence or absence of a radio signal , since all communications were in Morse code .
A minimum detectable signal is a signal at the input of a system whose power allows it to be detected over the background electronic noise of the detector system. It can alternately be defined as a signal that produces a signal-to-noise ratio of a given value m at the output. In practice, m is usually chosen to be greater than unity.
Ideal observer analysis is a method for investigating how information is processed in a perceptual system. [1] [2] [3] It is also a basic principle that guides modern research in perception.
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.