DSP Blockset

Block LMS Filter

Compute the filtered output, filter error, and filter weights for a given input and desired signal using the Block LMS adaptive filter algorithm

Library

Filtering / Adaptive Filters

Description

The Block LMS Filter block implements an adaptive least mean-square (LMS) filter, where the adaptation of filter weights occurs once for every block of data samples. The block estimates the filter weights, or coefficients, needed to convert the input signal into the desired signal. Connect the signal you want to filter to the Input port. This input can be a sample-based or frame-based signal. Connect the signal you want to model to the Desired port. The desired signal must have the same data type, signal type (sample or frame based), and dimensions as the input signal. The Output port outputs the filtered input signal, which can be sample or frame based. The Error port outputs the result of subtracting the output signal from the desired signal.

The block calculates the filter weights using the Block LMS algorithm. This algorithm is defined by the following equations.

The weight update function for the Block LMS Filter is defined as

where .

The variables are as follows.

Variable	Description
n	The current time index
i	The iteration variable in each block,
k	The block number
N	The block size
u	The vector of buffered input samples
	The vector of filter-tap estimates
y(n)	The filtered output
e(n)	The estimation error at time n
d(n)	The desired response at time n
µ	The adaptation step size

Use the Filter length parameter to specify the length of the filter weights vector.

The Block size parameter determines how many samples of the input signal are acquired before the filter weights are updated. The input frame length must be a multiple of the Block size parameter.

The adaptation Step-size (mu) parameter corresponds to µ in the equations. You can either specify a step-size using the input port, Step-size, or enter a value in the Block Parameters: Block LMS Filter dialog box.

Use the Leakage factor (0 to 1) parameter to specify the leakage factor, , in the leaky LMS algorithm shown below.

Enter the initial filter weights as a vector or a scalar in the Initial value of filter weights text box. If you enter a scalar, the block uses the scalar value to create a vector of filter weights. This vector has length equal to the filter length and all of its values are equal to the scalar value

If you select the Enable/disable adaptation via input port check box, an Adapt port appears on the block. When the input to this port is nonzero, the block continuously updates the filter weights. When the input to this port is zero, the filter weights remain at their current values.

If you want to reset the value of the filter weights to their initial values, use the Reset input parameter. The block resets the filter weights whenever a reset event is detected at the Reset port. The reset signal rate must be the same rate as the data signal input.

From the Reset input list, select None to disable the Reset port. To enable the Reset port, select one of the following from the Reset input list:

• Rising edge -- Triggers a reset operation when the Reset input does one of the following:
• Rises from a negative value to a positive value or zero
• Rises from zero to a positive value, where the rise is not a continuation of a rise from a negative value to zero (see the following figure).

• Falling edge -- Triggers a reset operation when the Reset input does one of the following:
• Falls from a positive value to a negative value or zero
• Falls from zero to a negative value, where the fall is not a continuation of a fall from a positive value to zero (see the following figure)
  
  
• Either edge -- Triggers a reset operation when the Reset input is a Rising edge or Falling edge (as described above)
• Non-zero sample -- Triggers a reset operation at each sample time that the Reset input is not zero

Note    When running simulations in the Simulink MultiTasking mode, sample-based reset signals have a one-sample latency, and frame-based reset signals have one frame of latency. Thus, there is a one-sample or one-frame delay between the time the block detects a reset event, and when it applies the reset. For more information on latency and the Simulink tasking modes, see Excess Algorithmic Delay (Tasking Latency) and the topic called The Simulation Parameters Dialog Box in the Simulink documentation.

Select the Output filter weights check box to create a Wts port on the block. For each iteration, the block outputs the current updated filter weights from this port.

Dialog Box

Filter length

Enter the length of the FIR filter weights vector.

Block size

Enter the number of samples to acquire before the filter weights are updated. The input frame length must be an integer multiple of the block size.

Specify step-size via

Select Dialog to enter a value for mu in the Block parameters: LMS Filter dialog box. Select Input port to specify mu using the Step-size input port.

Step-size (mu)

Enter the step-size. Tunable.

Leakage factor (0 to 1)

Enter the leakage factor, . Tunable.

Initial value of filter weights

Specify the initial values of the FIR filter weights.

Enable/disable adaptation via input port

Select this check box to enable the Adapt input port.

Reset input

Select this check box to enable the Reset input port.

Output filter weights

Select this check box to export the filter weights from the Wts port.

References

Hayes, M.H. Statistical Digital Signal Processing and Modeling. New York: John Wiley & Sons, 1996.

Supported Data Types

• Double-precision floating point
• Single-precision floating point

To learn how to convert your data types to the above data types in MATLAB and Simulink, see Supported Data Types and How to Convert to Them.

See Also

Kalman Adaptive Filter	DSP Blockset
LMS Filter	DSP Blockset
RLS Filter	DSP Blockset
Fast Block LMS Filter	DSP Blockset

See "Adaptive Filters for related information.

Backward Substitution

Buffer