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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
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.
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.
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.
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.
Signal sampling representation. The continuous signal S(t) is represented with a green colored line while the discrete samples are indicated by the blue vertical lines. In signal processing, sampling is the reduction of a continuous-time signal to a discrete-time signal. A common example is the conversion of a sound wave to a sequence of "samples".
Signal refers to both the process and the result of transmission of data over some media accomplished by embedding some variation. Signals are important in multiple subject fields including signal processing, information theory and biology. In signal processing, a signal is a function that conveys information about a phenomenon. [1]