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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).
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.
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.
Natural scene statistics are the basis for calculating ideal performance in natural and pseudo-natural tasks. This calculation tends to incorporate elements of signal detection theory , information theory , or estimation theory .
Statistical signal processing – analyzing and extracting information from signals and noise based on their stochastic properties; Linear time-invariant system theory, and transform theory; Polynomial signal processing – analysis of systems which relate input and output using polynomials; System identification [8] and classification ...
Detection theory, or signal detection theory, is a means to quantify the ability to discern between signal and noise. ... Statistics; Cookie statement;
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.
However, it also has important drawbacks. First, the threshold estimation is based only on p(yes), namely on "Hit" in Signal Detection Theory terminology. Second, and consequently, it is not bias free or criterion free. Third, the threshold is identified with the p(yes) = .5, which is just a conventional and arbitrary choice.