User`s guide

RLS Adaptive Filter
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The block icon has port labels corresponding to the inputs and outputs of the
RLS algorithm. Note that inputs to the
In and Err ports must be sample-based
scalars. The signal at the
Out port is a scalar, while the signal at the Taps port
is a sample-based vector.
An optional
Adapt input port is added when the Adapt input check box is
selected in the dialog box. When this port is enabled, the block continuously
adapts the filter coefficients while the
Adapt input is nonzero. A zero-valued
input to the
Adapt port causes the block to stop adapting, and to hold the filter
coefficients at their current values until the next nonzero
Adapt input.
The implementation of the algorithm in the block is optimized by exploiting the
symmetry of the inverse correlation matrix P(n). This decreases the total
number of computations by a factor of two.
The
FIR filter length parameter specifies the length of the filter that the RLS
algorithm estimates. The
Memory weighting factor corresponds to λ in the
equations, and specifies how quickly the filter “forgets” past sample
information. Setting λ=
1 specifies an infinite memory; typically, 0.95 ≤λ≤1.
The
Initial value of filter taps specifies the initial value as a vector, or
as a scalar to be repeated for all vector elements. The initial value of P(n) is
where is specified by the
Initial input variance estimate parameter.
Example The rlsdemo demo illustrates a noise cancellation system built around the RLS
Adaptive Filter block.
Block Ports Corresponding Variables
In
u, the scalar input, which is internally buffered into the
vector u(n)
Out
y(n), the filtered scalar output
Err
e(n), the scalar estimation error
Taps
, the vector of filter-tap estimates w
ˆ
n()
w
ˆ
0()
I
1
σ
ˆ
2
------
σ
ˆ
2