Time delay LCMV beamformer
TimeDelayLCMVBeamformer object implements
a time-delay linear constraint minimum variance beamformer.
To compute the beamformed signal:
Starting in R2016b, instead of using the
to perform the operation defined by the System
object™, you can
call the object with arguments, as if it were a function. For example,
= step(obj,x) and
y = obj(x) perform
H = phased.TimeDelayLCMVBeamformer creates
a time-delay linear constraint minimum variance (LCMV) beamformer System
The object performs time delay LCMV beamforming on the received signal.
H = phased.TimeDelayLCMVBeamformer( creates
a time-delay LCMV beamformer object,
each specified property Name set to the specified Value. You can specify
additional name-value pair arguments in any order as (
Handle to sensor array
Specify the sensor array as a handle. The sensor array must
be an array object in the
Signal propagation speed
Specify the propagation speed of the signal, in meters per second, as a positive scalar.
Default: Speed of light
Signal sampling rate
Specify the signal sampling rate (in hertz) as a positive scalar.
FIR filter length
Specify the length of the FIR filter behind each sensor element in the array as a positive integer.
Specify the constraint matrix used for time-delay LCMV beamformer
as an M-by-K matrix. Each column
of the matrix is a constraint and M 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
Desired response vector
Specify the desired response used for time-delay LCMV beamformer
as a column vector of length K, where K is
the number of constraints in the
Diagonal loading factor
Specify the diagonal loading factor as a positive scalar. Diagonal loading is a technique used to achieve robust beamforming performance, especially when the sample support is small. This property is tunable.
Add input to specify training data
To specify additional training data, set this property to
Source of beamforming direction
Specify whether the beamforming direction comes from the
Specify the beamforming direction of the beamformer as a column
vector of length 2. The direction is specified in the format of
Output beamforming weights
To obtain the weights used in the beamformer, set this property
|step||Perform time-delay LCMV beamforming|
Apply a time delay LCMV beamformer to an 11-element acoustic ULA array. The elements are omnidirectional microphones. The incident angle of the signal is -50 degrees in azimuth and 30 degrees in elevation. The incident signal is an FM chirp with 500 Hz bandwidth. The propagation speed is a typical speed of sound in air, 340 m/s.
Simulate the signal and add noise.
nElem = 11; microphone = phased.OmnidirectionalMicrophoneElement(... 'FrequencyRange',[20 20000]); array = phased.ULA('Element',microphone,'NumElements',nElem,'ElementSpacing',0.04); fs = 8000; t = 0:1/fs:0.3; x = chirp(t,0,1,500); c = 340; collector = phased.WidebandCollector('Sensor',array,... 'PropagationSpeed',c,'SampleRate',fs,... 'ModulatedInput',false); incidentAngle = [-50;30]; x = collector(x.',incidentAngle); noise = 0.2*randn(size(x)); rx = x + noise;
Create and apply the time-delay LCMV beamformer. Specify a filterlength of 5.
filterLength = 5; constraintMatrix = kron(eye(filterLength),ones(nElem,1)); desiredResponseVector = eye(filterLength,1); beamformer = phased.TimeDelayLCMVBeamformer('SensorArray',array,... 'PropagationSpeed',c,'SampleRate',fs,'FilterLength',filterLength,... 'Direction',incidentAngle,'Constraint',constraintMatrix,... 'DesiredResponse',desiredResponseVector); y = beamformer(rx);
Compare the beamformer output to the input to the middle sensor.
plot(t,rx(:,6),'r:',t,y) xlabel('Time') ylabel('Amplitude') legend('Original','Beamformed')
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.
 Frost, O. “An Algorithm For Linearly Constrained Adaptive Array Processing”, Proceedings of the IEEE. Vol. 60, Number 8, August, 1972, pp. 926–935.
 Van Trees, H. Optimum Array Processing. New York: Wiley-Interscience, 2002.
Usage notes and limitations:
Requires dynamic memory allocation. See Limitations for System Objects that Require Dynamic Memory Allocation.
See System Objects in MATLAB Code Generation (MATLAB Coder).