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Beamforming is the spatial equivalent of frequency filtering and can be grouped into two classes: data independent (conventional) and data-dependent (adaptive). All beamformers are designed to emphasize signals coming from some directions and suppress signals and noise arriving from other directions.

Phased Array System Toolbox™ provides nine different beamformers. This table summarizes the main properties of the beamformers.

Beamformer Name | Conventional or Adaptive | Bandwidth | Processing Domain |
---|---|---|---|

`phased.PhaseShiftBeamformer` | Conventional | Narrowband | Time domain |

`phased.TimeDelayBeamformer` | Conventional | Wideband | Time domain |

`phased.SubbandPhaseShiftBeamformer` | Conventional | Wideband | Frequency domain |

`phased.LCMVBeamformer` | Adaptive | Narrowband | Frequency domain |

`phased.MVDRBeamformer` | Adaptive | Narrowband | Frequency domain |

`phased.FrostBeamformer` | Adaptive | Wideband | Time domain |

`phased.GSCBeamformer` | Adaptive | Wideband | Time domain |

`phased.TimeDelayLCMVBeamformer` | Adaptive | Wideband | Time domain |

`phased.SubbandMVDRBeamformer` | Adaptive | Wideband | Frequency domain |

Conventional beamforming, also called classical beamforming, is the easiest to understand. Conventional beamforming techniques include delay-and-sum beamforming, phase-shift beamforming, subband beamforming, and filter-and-sum beamforming. These beamformers are similar because the weights and parameters that define the beampattern are fixed and do not depend on the array input data. The weights are chosen to produce a specified array response to the signals and interference in the environment. A signal arriving at an array has different times of arrival at each sensor. For example, plane waves arriving at a linear array have a time delay that is a linear function of distance along the array. Delay-and-sum beamforming compensates for these delays by applying a reverse delay to each sensor. If the time delay is accurately computed, the signals from each sensor add constructively.

Finding the compensating delay at each sensor requires accurate knowledge of the
sensor locations and signal direction. The delay-and-sum beamformer can be
implemented in the frequency domain or in the time domain. When the signal is
narrowband, time delay becomes a phase shift in the frequency domain and is
implement by multiplying each sensor signal by a frequency-dependent compensatory
phase shift. This algorithm is implemented in the `phased.PhaseShiftBeamformer`

. For broadband signals, there are several
approaches. One approach is to delay the signal in time by a discrete number of
samples. A problem with this method is that the degree of resolution that you can
distinguish is determined by the sampling rate of your data, because you cannot
resolve delay differences less than the sampling interval. Because this technique
only works if the sampling rate is high, you must increase the sampling frequency
well beyond the Nyquist frequency so that the true delay is very close to a sample
time. A second method interpolates the signal between samples. Time delay
beamforming is implemented in `phased.TimeDelayBeamformer`

. A third method Fourier transforms the
signals to the frequency domain, applies a linear phase shift, and converts the
signal back into the time domain. Phase-shift beamforming is performed at each
frequency band (see `phased.SubbandPhaseShiftBeamformer`

).

Beamforming is not limited to plane waves but can be applied even when there is wavefront curvature. In this case, the source lies in the near field. Perhaps the term beamforming is no longer appropriate. You can use the source-array geometry to compute the phase shift for each point in space and then apply this phase shift at each sensor element.

The advantage of a conventional beamformer is simplicity and ease of implementation. Another advantage is its robustness against pointing errors and signal direction errors. A disadvantage is its broad main lobe which decreases resolution of closely spaced sources or targets. A second disadvantage is that it has large sidelobes that allow interference sources to leak into the main beam.

The second class of beamformers consists of the data-dependent beamformers. The
terms optimal or adaptive beamformers are sometimes used for this class
interchangeably but they are not quite the same. Optimal beamformers apply weights
that are determined by optimizing some quantity. The MVDR beamformer determines the
beamforming weights, *w*, by maximizing the
signal-to-noise+interference ratio of the array output

$$\frac{{\left|{w}^{\prime}s\right|}^{2}}{{w}^{\prime}{R}_{n}w}={A}^{2}\frac{{\left|{w}^{\prime}a\right|}^{2}}{{w}^{\prime}{R}_{n}w}$$

where *s* represents the signal values at the
sensors, *a* represents the source steering vector, and
*A*^{2} represents the source power at
the array. *R _{n}* is the noise+interference
covariance matrix. Because the SNR is invariant under any scale factor applied to
the weights, an equivalent formulation of this criterion is to minimize the noise
output

$${w}^{\prime}{R}_{n}w\text{s}\text{.t}\text{.}w\text{'}a=1$$

The solution of this equation is

$${w}_{\text{opt}}=\frac{{R}_{\text{n}}{}^{-1}a}{a\text{'}{R}_{\text{n}}{}^{-1}a}$$

and yields the minimum variance distortionless response (MVDR)
beamformer. Because of the constraint, beamformer preserves the desired signal while
minimizing contributions to the array output due to noise and interference. The MVDR
beamformer is implemented in `phased.MVDRBeamformer`

. A broadband version is implemented in `phased.SubbandMVDRBeamformer`

.

There are several advantages to the MVDR beamformer.

The beamformer incorporates the noise and interference into an optimal solution.

The beamformer has higher spatial resolution than a conventional beamformer.

The beamformer puts nulls in the direction of any interference sources.

Sidelobes are smaller and smoother.

There are two major disadvantages to the MVDR beamformer. The MVDR beamformer is sensitive to errors in either the array parameters or arrival direction. The MVDR beamformer is susceptible to self-nulling. In addition, trying to use MVDR as an adaptive beamformer requires a matrix inversion every time the noise and interference statistics change. When there are many array elements, the inversion can be computationally expensive.

In practical applications, an accurate steering vector and an accurate covariance matrix are not always available. Generally, all that is available is the sampled covariance matrix. This deficiency can lead to both inadequate interference suppression and distortion of the desired signal. In this case, the true signal direction is slightly off from the beam pointing direction. Then the actual signal is treated as interference.

However it often turns out that the noise is not separable from the signal and it
is impossible to determine *R*_{n}. In that
case, you can estimate a sample covariance matrix from the data.

$${\widehat{R}}_{x}=\frac{1}{K}{\displaystyle \sum _{k=1}^{K}x(k){x}^{\prime}}(k)$$

and minimizes *w'R _{x}w*
instead. Minimizing this quantity leads to the minimum power distortionless response
(MPDR) beamformer. If the data vector,

Rewrite the direction constraint in the form *a’w* = 1 by
transposing both sides. This equivalent form suggests that it possible to include
multiple constraints by using a matrix constraint *Cw = d* where
*C* is now a constraint matrix and *d*
represents the signal gains due to the constraints. This is the form used in the
linear constraint minimum variance (LCMV) beamformer. The LCMV beamformer is a
generalization of MVDR beamforming and is implemented in `phased.LCMVBeamformer`

and `phased.TimeDelayLCMVBeamformer`

. There are several different approaches
to specifying constraints such as amplitude and derivative constraints. You can, for
example, specify weights that suppress interfering signals arriving from a
particular direction while passing signals from a different direction without
distortion. The optimal LCMV weights are determined by the equation

$${w}_{\text{opt}}={R}_{\text{n}}{}^{-1}{C}^{\prime}(C{R}_{\text{n}}{}^{-1}C\text{'}{)}^{-1}d$$

The advantages and disadvantages of the MVDR beamformer also apply to the LCMV beamformer.

While MVDR and LCMV are adaptive in principle, re-computation of the weights
requires the inversion of a potentially large covariance matrix when the array has
many elements. The Frost and generalized sidelobe cancelers are reformulations of
LCMV that convert the constrained optimization into minimizing an unconstrained form
and then compute the weights recursively. This approach removes any need to invert a
covariance matrix. See `phased.FrostBeamformer`

and `phased.GSCBeamformer`

.