You can use DSP System Toolbox™ blocks and System objects to measure the moving statistics and stationary statistics of signals in MATLAB® and Simulink®. Moving statistics refer to the statistics of streaming signals that change with time. In the sliding window method for computing moving statistics, a window of specified length moves over the data sample by sample as the new data comes in. The objects and blocks compute the statistics of the data within this window. The exponential weighting method applies a set of weights to the data samples and processes the weighted data. These weights are computed recursively based on the age of the data. For stationary statistics, the blocks and objects compute the statistics of all the data that is available in a batch.
|Autocorrelation||Autocorrelation of N-D array|
|Correlation||Cross-correlation of two inputs|
|Maximum||Maximum values of input or sequence of inputs|
|Mean||Find mean value of input or sequence of inputs|
|Median||Median value of input|
|Minimum||Minimum values of input or sequence of inputs|
|RMS||Root mean square value of input or sequence of inputs|
|Sort||Sort input elements by value|
|Standard Deviation||Standard deviation of input or sequence of inputs|
|Variance||Variance of input or sequence of inputs|
Learn how moving statistics are calculated.
Learn the differences between the sliding window method and exponential weighting method.
Moving average filter is a special case of the FIR filter.
Compute the moving average of streaming signals using MATLAB functions and System objects.
Remove high-frequency noise using a median filter.
Detect the event when the signal energy crosses a particular threshold value.
Learn how to compute the basic pulse and transition metrics of streaming signals.
List of System objects which support variable-sized signals in DSP System Toolbox.