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Moving minimum


The dsp.MovingMinimum System object™ determines the moving minimum of the input signal along each channel, independently over time. The object uses the sliding window method to determine the moving minimum. In this method, a window of specified length is moved over each channel, sample by sample, and the object determines the minimum of the data in the window. For more details, see Algorithms.

To determine the moving minimum of the input:

  1. Create the dsp.MovingMinimum object and set its properties.

  2. Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?



movMin = dsp.MovingMinimum returns a moving minimum object, movMin, using the default properties.


movMin = dsp.MovingMinimum(Len) sets the WindowLength property to Len.

movMin = dsp.MovingMinimum(Name,Value) specifies additional properties using Name,Value pairs. Unspecified properties have default values.

Example: movMin = dsp.MovingMinimum('SpecifyWindowLength',1,'WindowLength',10);


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Unless otherwise indicated, properties are nontunable, which means you cannot change their values after calling the object. Objects lock when you call them, and the release function unlocks them.

If a property is tunable, you can change its value at any time.

For more information on changing property values, see System Design in MATLAB Using System Objects.

Flag to specify a window length, specified as a scalar boolean.

  • true — The length of the sliding window is equal to the value you specify in the WindowLength property.

  • false — The length of the sliding window is infinite. In this mode, the object determines the minimum of the current sample and all the past samples.

Length of the sliding window in samples, specified as a positive scalar integer.


This property applies when you set SpecifyWindowLength to true.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64




y = movMin(x) determines the moving minimum of the input signal, x, using the sliding window method.

Input Arguments

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Data input, specified as a vector or a matrix. If x is a matrix, each column is treated as an independent channel. The moving minimum is determined along each channel. The object also accepts variable-size inputs. Once the object is locked, you can change the size of each input channel, but you cannot change the number of channels.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Output Arguments

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Moving minimum output, returned as a vector or a matrix.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Object Functions

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:


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stepRun System object algorithm
releaseRelease resources and allow changes to System object property values and input characteristics
resetReset internal states of System object


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Compute the moving minimum of a sum of three sine waves with varying amplitude. Use a sliding window of length 30.


Set up an input signal that is a sum of three sine waves with frequencies at 2 Hz, 5 Hz, and 10 Hz. The sampling frequency is 100 Hz. Create a dsp.MovingMinimum object with a window length of 30. Create a time scope for viewing the output.

sin = dsp.SineWave('SampleRate',100,...
    'Frequency',[2 5 10],...
movMin = dsp.MovingMinimum(30);
scope  = timescope('SampleRate',100,...
    'ShowGrid',true,'YLimits',[-4.5 4.5]);

Compute the Moving Minimum

Each sine wave component of the input signal has a different amplitude that varies with the iteration. Use the movMin object to determine the minimum value of the current sample and the past 29 samples of the input signal.

for index = 1:100
    sin.Amplitude = rand(1,3);
    x = sum(sin(),2);
    xmin = movMin(x);
scope.AxesScaling = 'onceatstop';


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

Extended Capabilities

Version History

Introduced in R2016b