Moving standard deviation

The `dsp.MovingStandardDeviation`

System
object™ computes the moving standard deviation of the input signal along each channel,
independently over time. The object uses either the sliding window method or the exponential
weighting method to compute the moving standard deviation. In the sliding window method, a
window of specified length is moved over the data, sample by sample, and the object computes
the standard deviation over the data in the window. In the exponential weighting method, the
object computes the exponentially weighted moving variance, and takes the square root. For
more details on these methods, see Algorithms.

To compute the moving standard deviation of the input:

Create the

`dsp.MovingStandardDeviation`

object and set its properties.Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects? (MATLAB).

returns
a moving standard deviation object, `MovStd`

= dsp.MovingStandardDeviation`MovStd`

, using the
default properties.

sets the `MovStd`

= dsp.MovingStandardDeviation(`Len`

)`WindowLength`

property to `Len`

.

specifies additional properties using `MovStd`

= dsp.MovingStandardDeviation(`Name,Value`

)`Name,Value`

pairs. Unspecified
properties have default values.

```
MovStd = dsp.MovingStandardDeviation('Method','Exponential
weighting','ForgettingFactor',0.999);
```

To use an object function, specify the
System
object as the first input argument. For
example, to release system resources of a System
object named `obj`

, use
this syntax:

release(obj)

[1] Bodenham, Dean. “Adaptive Filtering and Change Detection for Streaming Data.” PH.D. Thesis. Imperial College, London, 2012.

`dsp.MedianFilter`

|`dsp.MovingAverage`

|`dsp.MovingMaximum`

|`dsp.MovingMinimum`

|`dsp.MovingRMS`

|`dsp.MovingVariance`