Constant false alarm rate (CFAR) detector
CFARDetector object implements a constant
false-alarm rate detector.
To perform the detection:
H = phased.CFARDetector creates a constant
false alarm rate (CFAR) detector System object™,
The object performs CFAR detection on the input data.
H = phased.CFARDetector( creates
H, with each specified property Name
set to the specified Value. You can specify additional name-value
pair arguments in any order as (
Specify the algorithm of the CFAR detector as a string. Values of this property are:
Rank of order statistic
Specify the rank of the order statistic as a positive integer
scalar. The value must be less than or equal to the value of the
Number of guard cells
Specify the number of guard cells used in training as an even integer. This property specifies the total number of cells on both sides of the cell under test.
Number of training cells
Specify the number of training cells used in training as an even integer. Whenever possible, the training cells are equally divided before and after the cell under test.
Methods of obtaining threshold factor
Specify whether the threshold factor comes from an automatic
Desired probability of false alarm
Specify the desired probability of false alarm as a scalar between
0 and 1 (not inclusive). This property applies only when you set the
Custom threshold factor
Specify the custom threshold factor as a positive scalar. This
property applies only when you set the
Output detection threshold
To obtain the detection threshold, set this property to
|clone||Create CFAR detector object with same property values|
|getNumInputs||Number of expected inputs to step method|
|getNumOutputs||Number of outputs from step method|
|isLocked||Locked status for input attributes and nontunable properties|
|release||Allow property value and input characteristics changes|
|step||Perform CFAR detection|
Perform cell-averaging CFAR detection on a given Gaussian noise vector with a desired probability of false alarm of 0.1. Assume that the data is from a square law detector and no pulse integration is performed. Use 50 cells to estimate the noise level and 1 cell to separate the test cell and training cells. Perform the detection on all cells of input.
rng(5); hdet = phased.CFARDetector('NumTrainingCells',50,... 'NumGuardCells',2,'ProbabilityFalseAlarm',0.1); N = 1000; x = 1/sqrt(2)*(randn(N,1)+1i*randn(N,1)); dresult = step(hdet,abs(x).^2,1:N); Pfa = sum(dresult)/N;
phased.CFARDetector uses cell averaging in
Identify the training cells from the input, and form
the noise estimate. The next table indicates how the detector forms
the noise estimate, depending on the
|Use the average of the values in all the training cells.|
|Select the greater of the averages in the front training cells and rear training cells.|
|Sort the values in the training cells in ascending order. Select
the Nth item, where N is the
value of the |
|Select the smaller of the averages in the front training cells and rear training cells.|
Multiply the noise estimate by the threshold factor to form the threshold.
Compare the value in the test cell against the threshold to determine whether the target is present or absent. If the value is greater than the threshold, the target is present.
For further details, see .
 Richards, M. A. Fundamentals of Radar Signal Processing. New York: McGraw-Hill, 2005.