npwgnthresh
Detection SNR threshold for signal in white Gaussian noise
Syntax
snrthresh = npwgnthresh(pfa)
snrthresh = npwgnthresh(pfa,numpulses)
snrthresh = npwgnthresh(pfa,numpulses,dettype)
snrthresh = npwgnthresh(pfa,numpulses,dettype,outscale)
Description
calculates
the SNR threshold in decibels for detecting a deterministic signal
in white Gaussian noise. The detection uses the NeymanPearson (NP)
decision rule to achieve a specified probability of false alarm, snrthresh
= npwgnthresh(pfa
)pfa
.
This function uses a squarelaw detector.
Note
The output of npwgnthresh
determines the detection threshold
required to achieve a particular Pfa. The threshold increases when pulse integration
is used in the receiver. This threshold is not the single sample SNR that is used as
an input to rocsnr
or as the output of rocpfa
, albersheim
, and shnidman
. For any fixed Pfa, you can decrease the single sample SNR
required to achieve a particular Pd when pulse integration is used in the receiver.
See Signal Detection in White Gaussian Noise and Source Localization Using Generalized Cross Correlation for examples of
how to use npwgnthresh
in a detection system.
specifies snrthresh
= npwgnthresh(pfa
,numpulses
)numpulses
as
the number of pulses used in the pulse integration.
specifies snrthresh
= npwgnthresh(pfa
,numpulses
,dettype
)dettype
as
the type of detection. A square law detector is used in noncoherent
detection.
specifies
the output scale.snrthresh
= npwgnthresh(pfa
,numpulses
,dettype
,outscale
)
Input Arguments

Probability of false alarm. 

Number of pulses used in the integration. Default: 

Detection type. Specifies the type of pulse integration used in the NP decision
rule. Valid choices for Default: 

Output scale. Specifies the scale of the output value as one of Default: 
Output Arguments

Detection threshold expressed in signaltonoise ratio in decibels
or linear if $${T}_{dB}=20{\mathrm{log}}_{10}{T}_{lin}$$ 
Examples
More About
References
[1] Kay, S. M. Fundamentals of Statistical Signal Processing: Detection Theory. Upper Saddle River, NJ: Prentice Hall, 1998.
[2] Richards, M. A. Fundamentals of Radar Signal Processing. New York: McGrawHill, 2005.
References
[1] Kay, S. M. Fundamentals of Statistical Signal Processing: Detection Theory. Upper Saddle River, NJ: Prentice Hall, 1998.
[2] Richards, M. A. Fundamentals of Radar Signal Processing. New York: McGrawHill, 2005.