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Signal Correlation (Auto-Correlation Matrix) (G Dataflow)

Version:
    Last Modified: January 9, 2017

    Computes the auto-correlation matrix of a signal.

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    reset

    A Boolean to determine initialization of the internal state of the node.

    True Initializes the internal states to zero.
    False Initializes the internal states to the final states from the previous call of this node.

    This node automatically initializes the internal state to zero on the first call and runs continuously until this input is True. For a large data sequence consisting of smaller blocks, when this input is True, this node calculates the histogram of the current block only and ignores previous blocks.

    Default: False

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    signal

    The input signal.

    This input can be a 2D array of double-precision, floating-point numbers or a 2D array of complex double-precision, floating-point numbers.

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    order

    Order of the auto-correlation matrix. If the order is smaller than zero, this node returns an error.

    Default: 0

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    method

    Method used to compute the auto-correlation matrix.

    Name Value Description
    AutoCorrelation 0 Uses the auto-correlation method.
    Pre-Windowed 1 Uses the pre-windowed method.
    Post-Windowed 2 Uses the post-windowed method.
    Covariance 3 Uses the covariance method.
    Modified Covariance 4 Uses the modified covariance method.

    Determining Which Method to Use

    Auto-correlation matrix is widely used in the field of spectrum analysis to estimate the spectral components within the input signal. In general, Covariance and Modified Covariance methods give better results in spectral estimation processing than the AutoCorrelation, Pre-Windowed and Post-Windowed methods. NI recommends that you use the Covariance or the Modified Covariance method to estimate the auto-correlation matrix when performing spectrum analysis.

    Algorithm Definition for the Auto-Correlation Method

    If method is AutoCorrelation, R is a data matrix of size (N+k)-by-(k+1) defined as follows.

    R = [ x 0 0 x k x 0 x N 1 x N k 1 0 x N 1 ]

    where

    • xi is the ith element in the input signal
    • N is the length of the input signal
    • k is order

    The normalization factor is equal to N.

    Algorithm Definition for the Pre-Windowed Method

    If method is Pre-Windowed, R is a matrix of size N-by-(k+1) defined as follows.

    R = [ x 0 0 x k x 0 x N 1 x N k 1 ]

    where

    • xi is the ith element in the input signal
    • N is the length of the input signal
    • k is order

    The normalization factor is equal to N.

    Algorithm Definition for the Post Windowed Method

    If method is Post-Windowed, R is a matrix of size N-by-(k+1) defined as follows.

    R = [ x k x 0 x N 1 x N k 1 0 x N 1 ]

    where

    • xi is the ith element in the input signal
    • N is the length of the input signal
    • k is order

    The normalization factor is equal to N.

    Algorithm Definition for the Covariance Method

    If method is Covariance, R is a matrix of size (N-k)-by-(k+1) defined as follows.

    R = [ x k x 0 x N 1 x N k 1 ]

    where

    • xi is the ith element in the input signal
    • N is the length of the input signal
    • k is order

    The normalization factor is equal to N-k.

    Algorithm Definition for the Modified Covariance Method

    If method is Modified Covariance, R is a matrix of size 2(N-k)-by-(k+1) defined as follows.

    R = [ x k x 0 x N 1 x N k 1 x 0 * x k * x N k 1 * x N 1 * ]

    where

    • xi is the ith element in the input signal
    • N is the length of the input signal
    • k is order
    • xi* is the complex conjugate of xi

    The normalization factor is equal to 2*(N-k).

    Default: AutoCorrelation

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    error in

    Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.

    Default: No error

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    autocorrelation matrix

    Auto-correlation matrix of the input signal.

    The size of the auto-correlation matrix is (order+1) multiplied by (order+1).

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    error out

    Error information. The node produces this output according to standard error behavior.

    Algorithm for Calculating the Auto-Correlation Matrix

    This node uses the following equation to calculate the auto-correlation matrix.

    M = R H * R s

    where

    • M is autocorrelation matrix
    • R is data matrix
    • s is normalization factor
    • RH is the conjugate transpose of matrixR

    Where This Node Can Run:

    Desktop OS: Windows

    FPGA: Not supported


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