Beamformer¶
Beamforming is split into the prototype classes TransmitBeamformer
and ReceiveBeamformer
for beamforming operations during signal transmission and reception, respectively.
They are both derived from the base BeamformerBase
.
This is due to the fact that some beamforming algorithms may be exclusive to transmission or reception usecases.
Should a beamformer be applicable during both transmission and reception both prototypes can be inherited.
An example for such an implementation is the Conventional
beamformer.
 class BeamFocus[source]¶
Bases:
ABC
,Serializable
Single focus point of a beamformer.
 abstract copy()[source]¶
Create a copy of this focus point.
 Return type:
TypeVar
(FT
, bound= BeamFocus) Returns:
A copy of this focus point.
 property beamformer: BeamformerBase  None¶
Beamformer this focus point is assigned to.
 class BeamformerBase(operator=None)[source]¶

Base class for all beam steering precodings.
Args:
 operator (OT, optional):
The operator this beamformer is attached to. By default, the beamformer is considered floating.
 class CoordinateFocus(coordinates, reference='local')[source]¶
Bases:
BeamFocus
Focus the beamformer towards a certain Cartesian coordinate.
 Parameters:
coordinates (numpy.ndarray) – Cartesian coordinates in m.
reference (str, optional) – Reference frame of the coordinates.
 class DeviceFocus(device)[source]¶
Bases:
BeamFocus
Focus point targeting a device.
 Parameters:
device (Device) – Device focused by the beamformer.
 class OT¶
Type of operator.
alias of TypeVar(‘OT’, bound=
Operator
)
 class ReceiveBeamformer(operator=None)[source]¶
Bases:
BeamformerBase
[Receiver
],ReceiveStreamDecoder
,ABC
Base class for beam steering precodings during signal receptions.
The beamformer is characterised by its required number of input streams \(N\), the resulting number of output streams \(M\) and the supported number of focus points \(F\). Considering a beamformer is provided with a matrix of \(T\) baseband samples \(\mathbf{X} \in \mathbb{C}^{N \times T}\), the beamforming process
\[\mathbf{Y} = \mathcal{B}\lbrace \mathbf{X} \rbrace \quad \text{with} \quad \mathbf{Y} \in \mathbb{C}^{M \times T}\]can generally be described as a function compressing the number of streams while focusing the power towards the angles of interest \(F\).
Args:
 operator (Receiver, optional):
The operator this beamformer is attached to. By default, the beamformer is considered floating.
 abstract _decode(samples, carrier_frequency, angles, array)[source]¶
Decode signal streams for receive beamforming.
This method is called as a subroutine during
receive()
andprobe()
. Parameters:
samples (np.ndarray) – Signal samples, first dimension being the number of signal streams \(N\), second the number of samples \(T\).
carrier_frequency (float) – The assumed carrier central frequency of the samples \(f_\mathrm{c}\).
angles (numpy.ndarray) – Spherical coordinate system angles of arrival in radians. A threedimensional numpy array with the first dimension representing the number of angles, the second dimension of magnitude number of focus points \(F\), and the third dimension containing the azimuth and zenith angle in radians, respectively.
array (AntennaArray) – The assumed antenna array.
 Return type:
 Returns:
Stream samples of the focused signal towards all focus points. A threedimensional numpy array with the first dimension representing the number of focus points, the second dimension the number of returned streams and the third dimension the amount of samples.
 decode_streams(streams)[source]¶
Encode a signal MIMO stream during signal recepeption.
This operation may modify the number of streams.
Returns: The decoded signal stream.
 probe(signal, focus_points=None)[source]¶
Focus a signal model towards certain directions of interest.
 Parameters:
signal (Signal) – The signal to be steered.
focus_points (np.ndarray, optional) – Focus point of the steered signal power. Twodimensional numpy array with the first dimension representing the number of points and the second dimension representing the point values.
 Return type:
 Returns:
Stream samples of the focused signal towards all focus points. A threedimensional numpy array with the first dimension representing the number of focus points, the second dimension the number of returned streams and the third dimension the amount of samples.
 receive(signal, focus=None, array=None)[source]¶
Focus a signal model towards a certain target.
 Parameters:
signal (Signal) – The signal to be steered.
focus (BeamFocus  Sequence[BeamFocus], optional) – Focus of the steered signal power. If not provided, the beamformer’s default
receive_focus
is used.array (AntennaArray, optional) – Antenna array assumed used for steering. If not specified, the operator’s antenna array is used.
 Return type:
 Returns:
Signal focused towards the requested focus points.
 Raises:
FloatingError – If the operator or operator device are not yet specified.
RuntimeError – If the number of signal streams does not match the number of required input streams.
ValueError – If the number of focus points does not match the number of required focus points.
 abstract property num_receive_focus_points: int¶
Number of required receive focus points.
If this is \(1\), the beamformer is considered to be a single focus point beamformer and
receive_focus
will return a single focus point. Otherwise, the beamformer is considered a multi focus point beamformer andreceive_focus
will return aSequence
of focus points.Returns: Number of focus points.
 abstract property num_receive_input_streams: int¶
Number of input streams required by this beamformer.
Dimension \(N\) of the input sample matrix \(\mathbf{X} \in \mathbb{C}^{N \times T}\).
 Returns:
Number of input streams \(N\).
 abstract property num_receive_output_streams: int¶
Number of output streams generated by this beamformer.
Dimension \(M\) of the output sample matrix \(\mathbf{Y} \in \mathbb{C}^{M \times T}\).
 Returns:
Number of output streams \(M\).
 property precoding: Precoding  None¶
Access the precoding configuration this precoder is attached to.
 Returns:
Handle to the precoding. None if the precoder is considered floating.
 Raises:
RuntimeError – If this precoder is currently floating.
 property probe_focus_points: ndarray¶
Default beamformer focus points during probing.
 Returns:
The focus points as a threedimensional numpy array, with the first dimension representing the probe index, the second dimension the point and the third dimension of magnitude two the point azimuth and zenith, respectively.
 Raises:
ValueError – On invalid arguments.
 property receive_focus: BeamFocus  Sequence[BeamFocus]¶
Focus points of the beamformer during reception.
Depending on
num_receive_focus_points
this is either a single focus point or aSequence
of points. Raises:
ValueError – If the provided number of focus points does not match the number of required focus points.
 class SphericalFocus(angles: ndarray)[source]¶
 class SphericalFocus(azimuth: float, zenith: float)
Bases:
BeamFocus
Focus point in spherical coordinates.
 class TransmitBeamformer(operator=None)[source]¶
Bases:
BeamformerBase
[Transmitter
],TransmitStreamEncoder
,ABC
Base class for beam steering precodings during signal transmissions.
Args:
 operator (Transmitter, optional):
The operator this beamformer is attached to. By default, the beamformer is considered floating.
 encode_streams(streams)[source]¶
Encode a signal MIMO stream during transmission.
This operation may modify the number of streams.
Returns: The encoded signal stream.
 transmit(signal, focus=None, array=None)[source]¶
Focus a signal model towards a certain target.
 Parameters:
signal (Signal) – The signal to be steered.
focus (BeamFocus  Sequence[BeamFocus], optional) – Focus points of the steered signal power. If None, the beamformer’s default
transmit_focus
is used.array (AntennaArray, optional) – Antenna array assumed used for steering. If None, the operator’s antenna array is used.
 Return type:
 Returns:
Samples of the focused signal.
 Raises:
RuntimeError – If the operator or operator device are not yet specified.
RuntimeError – If the number of signal streams does not match the number of required input streams.
ValueError – If the number of focus points does not match the number of required focus points.
 abstract property num_transmit_focus_points: int¶
Number of required transmit focus points.
If this is \(1\), the beamformer is considered to be a single focus point beamformer and
transmit_focus
will return a single focus point. Otherwise, the beamformer is considered a multi focus point beamformer andtransmit_focus
will return aSequence
of focus points.Returns: Number of focus points.
 abstract property num_transmit_input_streams: int¶
Number of input streams required by this beamformer.
 Returns:
Number of input streams.
 abstract property num_transmit_output_streams: int¶
Number of output streams generated by this beamformer.
 Returns:
Number of output streams.
 property precoding: Precoding  None¶
Access the precoding configuration this precoder is attached to.
 Returns:
Handle to the precoding. None if the precoder is considered floating.
 Raises:
RuntimeError – If this precoder is currently floating.
 property transmit_focus: BeamFocus  Sequence[BeamFocus]¶
Focus points of the beamformer during transmission.
Depending on
num_transmit_focus_points
this is either a single focus point or aSequence
of points. Raises:
ValueError – If the provided number of focus points does not match the number of required focus points.