GLRT Detector
Block GLRT-receiver detection algorithm.
blockType: GLRTDetector
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Path in the library: |
Description
Block GLRT Detector performs detection of signals with unknown parameters in the presence of noise. Unknown parameters include amplitude, phase, frequency and time of arrival of the signal. The detector replaces the unknown parameters with their maximum likelihood estimates under the hypothesis of the absence of a signal and an alternative hypothesis of the presence of a signal . The binary detector then chooses between the null hypothesis and an alternative hypothesis based on measurements. If the hypothesis the best way to take into account the data, the detector determines that the target is missing. If the hypothesis Best matched to the data, the detector determines that the target is present.
Ports
Input
#
X
—
The input signal
real vector N by 1 | real vector N by 1 | real matrix N by M | complex matrix N by M
Details
Input data specified as a real or complex vector on or a real or complex matrix on . – this is the length of the signal, and – the number of data channels. Detection is performed along the X columns. The size of each row it cannot change during simulation.
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If , X represents a single data channel.
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If , X can represent samples of samples from data channels. The data streams can be combined later, for example, using beamforming.
The input data has a common interpretation. For example, the data can be interpreted as:
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Time series – time series samples.
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Sensor – It is a snapshot of a sample of samples from a set of sensors.
| Data types |
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| Complex numbers support |
Yes |
#
Hyp
—
augmented linear matrix of equality constraints
real matrix R on (P+1) | complex matrix R on (P+1)
Details
Augmented linear equality constraint matrix, defined as a real or complex matrix on . The matrix has the form [A, b] and is the equation:
where unknown parameters are contained in a variable . A has a rank of . The augmented linear equality constraint matrix expresses the null hypothesis .
For this signal model, the GLRT detector determines whether to reject the null hypothesis, which is expressed in the form aΘ = b, where A is the matrix on with a rank and by rank , a b is a vector on . A and b are in the augmented linear equality constraint matrix hyp = [A, b]. Because there is For signal models, the GLRT detector outputs the detection results for each column X.
| Data types |
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| Complex numbers support |
Yes |
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Obs
—
the observation matrix
array of N by P by D
Details
An observation matrix for a linear deterministic signal model, specified as an array on on , where , rank , is the number of signal models, and white Gaussian noise is a vector on , determined by the covariance argument `ncov'. The observation matrix is defined as `X =obs*param+ noise'.
- Example
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30.0
| Data types |
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| Complex numbers support |
Yes |
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NCov
—
preset noise power
positive scalar
Details
The noise power, specified as a scalar or vector, is a string of length .
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If nCoV is a scalar, it represents an equal known noise power for models.
If nCoV is a vector string of length , it represents the specified noise power for models, respectively.
Dependencies
To use this port, check the box for the Enable known noise power input option.
| Data types |
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| Complex numbers support |
No |
Output
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Y
—
detection results
logical vector D by M | vector of integers 1 by L | matrix of integers 2 by L
Details
Detection results models for independent data samples returned as a logical vector on pr. The Y format depends on the value of the Output format parameter. By default, the Output format parameter is set to `Detection result'.
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If the Output format parameter is set to
Detection result, Y is the matrix on , containing the results of logical detection, where – this is the number of signal models, and – number of columns X. For each row – it istruein the column if there is a detection in the corresponding column `arg'. Otherwise, Y is `false'. -
If the Output format parameter is set to
Detection index, Y is a vector on or the matrix on , containing the detection indexes, where – this is the number of detections found in data samples and models . If X is a column vector, – vector on , containing the index of detections found in models . When X is a matrix, Y is a matrix on , and each column it has the format[detrow;detcol], wheredetrowis the index of the model, anddetcolis the index of the column .
| Data types |
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| Complex numbers support |
No |
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Stat
—
detection statistics
matrix N on (default) | vector 1 on L
Details
Detection statistics returned as a matrix on or vectors on . The value of Stat depends on the value of the Output format parameter.
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If the Output format parameter is set to
Detection result, Stat has the same size as Y. -
If the Output format parameter is set to
Detection index, Stat is a vector on , containing detection statistics for each corresponding detection in Y.
Dependencies
To use this port, check the box for the Output detection statistics and threshold parameter.
| Data types |
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| Complex numbers support |
No |
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Th
—
calculated detection threshold
scalar
Details
The detection threshold returned as a scalar.
Dependencies
To use this port, check the box for the Output detection statistics and threshold parameter.
| Data types |
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| Complex numbers support |
No |
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Param
—
estimates of the maximum likelihood of signal parameters
array P by N by D
Details
Maximum likelihood estimates (MLE) of unknown signal parameters returned as an array on on .
Dependencies
To use this port, check the box for the Output MLEs of unknown signal parameters parameter.
| Data types |
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| Complex numbers support |
No |
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N
—
noise power
positive scalar
Details
The estimated noise power returned as a positive scalar.
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If the Output format parameter is set to
Detection result, N has the same size as Y. -
If the Output format parameter is set to
Detection index, N returns a noise power estimate with the size on for each corresponding detection in Y.
Dependencies
To use this port, check the box for the Output estimated noise power parameter.
| Data types |
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| Complex numbers support |
No |
Parameters
Main
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Probability of false alarm —
the probability of a false alarm
Real number
Details
The probability of a false alarm, set as a positive scalar from 0 to 1 inclusive.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
Yes |
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Output format —
format of the detection results
Detection result | Detection index
Details
The format of the detection results returned to the output port Y is set as Detection result or `Detection index'.
| Values |
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| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
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Output detection statistics and threshold —
output of detection and threshold statistics
Logical
Details
Select this option to display detection statistics and detection threshold via ports Stat and Th.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
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Enable known noise power input (NCov) —
turning on the noise input power
Logical
Details
Select this option to enable noise power input via the nCoV port.
Dependencies
To check this box, uncheck the Output estimated noise power box.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
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Output MLEs of unknown signal parameters —
enabling maximum likelihood estimation output
Logical
Details
Select this option to output a maximum likelihood estimate of the signal parameters via the Param port.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |
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Output estimated noise power —
enabling the output of the calculated noise power
Logical
Details
Select this option to output the estimated noise power via port N.
| Default value |
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| Program usage name |
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| Tunable |
No |
| Evaluatable |
No |