# rocpfa

Receiver operating characteristic curves by false-alarm probability

## Description

[Pd,SNR] = rocpfa(Pfa) returns the single-pulse detection probabilities, Pd, and required SNR values, SNR, for the false-alarm probabilities in the row or column vector Pfa. By default, for each false-alarm probability, the detection probabilities are computed for 101 equally spaced SNR values between 0 and 20 dB. The ROC curve is constructed assuming a single pulse in coherent receiver with a nonfluctuating target.

[Pd,SNR] = rocpfa(Pfa,Name=Value) returns detection probabilities and SNR values with additional options specified by one or more name-value arguments.

example

rocpfa(...) plots the ROC curves.

## Examples

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Plot ROC curves for false-alarm probabilities of 1e-8, 1e-6, and 1e-3, assuming no pulse integration.

Pfa = [1e-8 1e-6 1e-3];
rocpfa(Pfa,SignalType="NonfluctuatingCoherent")

Examine the effect of SNR on the probability of detection for a detector using noncoherent integration with a false-alarm probability of 1e-4. Assume the target has a nonfluctuating RCS and that you are integrating over 5 pulses.

[Pd,SNR] = rocpfa(1e-4,...
'SignalType','NonfluctuatingNoncoherent',...
'NumPulses',5);
figure;
plot(SNR,Pd); xlabel('SNR (dB)');
ylabel('Probability of Detection'); grid on;
title('Nonfluctuating Noncoherent Detector (5 Pulses)');

## Input Arguments

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False-alarm probabilities, specified as a row or column vector.

Example: [1e-8 1e-6 1e-3]

Data Types: double

### Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: MaxSNR=15,NumPoints=64,NumPulses=10

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: 'MaxSNR',15,'NumPoints',64,'NumPulses',10

Maximum SNR to include in the ROC calculation, specified as a positive scalar.

Data Types: double

Minimum SNR to include in the ROC calculation, specified as a positive scalar.

Data Types: double

Number of SNR values to use when calculating the ROC curves, specified as a positive integer. The actual values are equally spaced between MinSNR and MaxSNR.

Data Types: double

Number of pulses to integrate when calculating the ROC curves, specified as a positive integer. A value of 1 indicates no pulse integration.

Data Types: double

This property specifies the type of received signal or, equivalently, the probability density functions (PDF) used to compute the ROC. Valid values are: "Real", "NonfluctuatingCoherent", "NonfluctuatingNoncoherent", "Swerling1", "Swerling2", "Swerling3", and "Swerling4". Values are not case sensitive.

The "NonfluctuatingCoherent" signal type assumes that the noise in the received signal is a complex-valued, Gaussian random variable. This variable has independent zero-mean real and imaginary parts each with variance σ2/2 under the null hypothesis. In the case of a single pulse in a coherent receiver with complex white Gaussian noise, the probability of detection, PD, for a given false-alarm probability, PFA is:

${P}_{D}=\frac{1}{2}\text{erfc}\left({\text{erfc}}^{-1}\left(2{P}_{FA}\right)-\sqrt{\chi }\right)$

where erfc and erfc-1 are the complementary error function and that function’s inverse, and χ is the SNR not expressed in decibels.

For details about the other supported signal types, see [1] .

Data Types: char | string

## Output Arguments

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Detection probabilities corresponding to the false-alarm probabilities, returned as a vector. For each false-alarm probability in Pfa, Pd contains one column of detection probabilities.

Signal-to-noise ratios, returned as a column vector. By default, the SNR values are 101 equally spaced values between 0 and 20. To change the range of SNR values, use the optional MinSNR or MaxSNR input argument. To change the number of SNR values, use the optional NumPoints input argument.

## References

[1] Richards, M. A. Fundamentals of Radar Signal Processing. New York: McGraw-Hill, 2005, pp 298–336.

## Version History

Introduced in R2011a