Y = step(H,X)
Y = step(H,X,XT)
Y = step(H,X,ANG)
[Y,W] = step(___)
Note:
Starting in R2016b, instead of using the |
Y = step(H,X)
performs time-delay LCMV
beamforming on the input, X
, and returns the
beamformed output in Y
. X
is
an M-by-N matrix where N is
the number of elements of the sensor array. M must
be larger than the FIR filter length specified in the FilterLength
property. Y
is
a column vector of length M.
The size of the first dimension of this input matrix can vary to simulate a changing signal length, such as a pulse waveform with variable pulse repetition frequency.
Y = step(H,X,XT)
uses XT
as
the training samples to calculate the beamforming weights when you
set the TrainingInputPort
property to true
. XT
is
an M-by-N matrix where N is
the number of elements of the sensor array. M must
be larger than the FIR filter length specified in the FilterLength
property.
Y = step(H,X,ANG)
uses ANG
as
the beamforming direction, when you set the DirectionSource
property
to 'Input port'
. ANG
is a
column vector of length 2 in the form of [AzimuthAngle; ElevationAngle]
(in
degrees). The azimuth angle must be between –180° and
180°, and the elevation angle must be between –90°
and 90°.
You can combine optional input arguments when their enabling
properties are set: Y = step(H,X,XT,ANG)
[Y,W] = step(___)
returns additional
output, W
, as the beamforming weights when you
set the WeightsOutputPort
property to true
. W
is
a column vector of length L, where L is
the number of degrees of freedom of the beamformer. For a time-delay
LCMV beamformer, the number of degrees of freedom is given by the
product of the number of elements of the array and the filter length
specified by the value of the FilterLength
property.
Note:
The object performs an initialization the first time the |
The beamforming algorithm is the time-domain counterpart of the narrowband linear constraint minimum variance (LCMV) beamformer. The algorithm does the following:
Steers the array to the beamforming direction.
Applies an FIR filter to the output of each sensor to achieve the specified constraints. The filter is specific to each sensor.