Constant false alarm rate (CFAR) detector
The 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™, H. The object performs CFAR detection on the input data.
H = phased.CFARDetector(Name,Value) creates object, H, with each specified property Name set to the specified Value. You can specify additional name-value pair arguments in any order as (Name1,Value1,...,NameN,ValueN).
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 NumTrainingCells property. This property applies only when you set the Method property to 'OS'.
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.
Default: 2, indicating that there is one guard cell at both the front and back 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.
Default: 2, indicating that there is one training cell at both the front and back of the cell under test
Methods of obtaining threshold factor
Specify whether the threshold factor comes from an automatic calculation, the CustomThresholdFactor property of this object, or an input argument in step. Values of this property are:
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 ThresholdFactor property to 'Auto'.
Custom threshold factor
Specify the custom threshold factor as a positive scalar. This property applies only when you set the ThresholdFactor property to 'Custom'. This property is tunable.
Output detection threshold
To obtain the detection threshold, set this property to true and use the corresponding output argument when invoking step. If you do not want to obtain the detection threshold, set this property to false.
|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 three steps:
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 Method property value.
|'CA'||Use the average of the values in all the training cells.|
|'GOCA'||Select the greater of the averages in the front training cells and rear training cells.|
|'OS'||Sort the values in the training cells in ascending order. Select the Nth item, where N is the value of the Rank property.|
|'SOCA'||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 .