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LRT Detector

Likelihood ratio test detector

Since R2023b

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  • LRT Detector

Libraries:
Phased Array System Toolbox / Detection

Description

The likelihood ratio test (LRT) detector performs binary signal detection in the presence of noise. The binary detector chooses between the null hypothesis H0 and the alternative hypothesis H1 based on data measurements. The null hypothesis denotes the absence of any signal while the alternative hypothesis denotes the presence of some signal.

Ports

Input

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Input data, specified as a real-valued or complex-valued N-by-1 vector, or an N-by-M real-valued or complex-valued matrix. N is the signal length and M is the number of data channels. Each data channel has N samples to yield an N-by-M matrix. Each row represents the components of a length-M data vector. When M = 1, X represents a single channel of data. When M > 1, X can represent N samples from M data channels. Detection is performed along the columns of X. The size of each row M cannot change during the simulation.

The LRT detector assumes the same signal model in each column of X. The noise-free signal is contained in the N-dimensional data vector Xknown. Gaussian noise, defined by the noise power NCov, is added to each column. For this signal model, the LRT detector determines whether or not to reject the null hypothesis Xknown = 0. Because there is only one known signal model, the LRT detector outputs one detection result for each column of X.

Data Types: single | double
Complex Number Support: Yes

Known noise-free signal, specified as a real-valued or complex-valued N-by-1 vector.

Data Types: single | double
Complex Number Support: Yes

Noise power or covariance, specified as a positive scalar.

Example: 2.0

Data Types: single | double

Output

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Detection results, returned as a 1-by-M logical-valued vector or a 1-by-L integer-valued vector. The format of Y depends on the value of the Output format parameter. By default, the Output format parameter is set to 'Detection result'.

  • When the Output format parameter is set to 'Detection result', Y is a 1-by-M vector containing logical detection results, where M is the number of columns of X. Y is true if there is a detection in the corresponding column of X. Otherwise, Y is false.

  • When the Output format parameter is set to 'Detection index', Y is a 1-by-L integer-valued vector containing detection indices, where L is the number of detections found over all M channels.

Data Types: single | double | Boolean
Complex Number Support: Yes

Detection statistics, returned as a D-by-M matrix or as a 1-by-L vector. The format of stat depends on the setting of the Output format parameter.

  • When Output format parameter is 'Detection result', stat is a D-by-M matrix.

  • When Output format parameter is 'Detection index', stat is a 1-by-L vector containing detection statistics for each corresponding detection in Y.

Dependencies

To enable this port, select the Output detection statistics and threshold check box.

Data Types: single | double
Complex Number Support: Yes

Detection threshold, returned as a scalar.

Dependencies

To enable this port, select the Output detection statistics and threshold check box.

Data Types: single | double

Parameters

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Probability of false alarm, specified as positive scalar between 0 and 1, inclusive.

Programmatic Use

Block Parameter:ProbabilityFalseAlarm
Type:double, single
Values:scalar |
Default:0.1

Data Types: single | double

Format of output data, specified as Detection result or Detection index. Output data is returned in the Y port.

Example: Detection index

Programmatic Use

Block Parameter:OutputFormat
Type:char, string
Values:Detection result | Detection index
Default:Detection result

Data Types: char | string

Select this checkbox to output detection statistics and detection threshold using the Stat and Th ports.

Programmatic Use

Block Parameter:ThresholdOutputPort
Type:check box
Values:0 | 1
Default:0

Block simulation, specified as Interpreted Execution or Code Generation. If you want your block to use the MATLAB® interpreter, choose Interpreted Execution. If you want your block to run as compiled code, choose Code Generation. Compiled code requires time to compile but usually runs faster.

Interpreted execution is useful when you are developing and tuning a model. The block runs the underlying System object™ in MATLAB. You can change and execute your model quickly. When you are satisfied with your results, you can then run the block using Code Generation. Long simulations run faster with generated code than in interpreted execution. You can run repeated executions without recompiling, but if you change any block parameters, then the block automatically recompiles before execution.

This table shows how the Simulate using parameter affects the overall simulation behavior.

When the Simulink® model is in Accelerator mode, the block mode specified using Simulate using overrides the simulation mode.

Acceleration Modes

Block SimulationSimulation Behavior
NormalAcceleratorRapid Accelerator
Interpreted ExecutionThe block executes using the MATLAB interpreter.The block executes using the MATLAB interpreter.Creates a standalone executable from the model.
Code GenerationThe block is compiled.All blocks in the model are compiled.

For more information, see Choosing a Simulation Mode (Simulink).

Programmatic Use

Block Parameter:SimulateUsing
Type:enum
Values:Interpreted Execution, Code Generation
Default:Interpreted Execution

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Version History

Introduced in R2023b

See Also