Digital antenna array


Digital antenna array is smart antenna with multi channels digital beamforming, usually by using fast Fourier transform.
The development and practical realization of digital antenna arrays theory started in 1962 under the guidance of Vladimir Varyukhin.

Digital beamforming

The main approach to digital signal processing in DAA is the "digital beamforming" after Analog-to-digital converters of receiver channels or before Digital-to-analog converters by transmission.
Digital beamforming of DAA has a big lot of advantages because in this case the digital signals massive can be transformed and combined in a few possible ways in parallel, to get different output signals. The signals from every direction can be estimated simultaneously and integrated for a longer time to increasing of signals energy when detecting far-off objects and simultaneously integrated for a shorter time to detecting fast-moving close objects.
Before digital beamforming operation should be used a correction of channels characteristics by a special test source or using the heterodyne signal.
Such correction can be used not only for receiving channels but also in transmission channels of active DAA.
Limitations on the accuracy of estimation of direction of arrival signals and depth of suppression of interferences in digital antenna arrays are associated with jitter ADCs and DACs.

Maximum likelihood beamformer

In maximum likelihood beamformer, the noise is modeled as a stationary Gaussian white random processes while the signal waveform as deterministic and unknown.

Bartlett beamformer

The Bartlett beamformer is a natural extension of conventional spectral analysis to the DAA. Its spectral power is represented by
The angle that maximizes this power is an estimation of the angle of arrival.

Capon beamformer

Capon beamformer, also known as the Minimum Variance Distortionless Response beamforming algorithm, has a power given by
Though the MVDR/Capon beamformer can achieve better resolution than the conventional approach, but this algorithm has higher complexity due to the full-rank matrix inversion. Technical advances in GPU computing have begun to narrow this gap and make real-time Capon beamforming possible.

MUSIC beamformer

MUSIC beamforming algorithm starts with decomposing the covariance matrix for both the signal part and the noise part. The eigen-decomposition of is represented by
MUSIC uses the noise sub-space of the spatial covariance matrix in the denominator of the Capon algorithm
Therefore MUSIC beamformer is also known as subspace beamformer. Compared to the Capon beamformer, it gives much better DOA estimation.
As an alternative approach can be used ESPRIT algorithm as well.

DAA Examples

Radars

MIMO systems

DAA use to improve the performance of radio communications in MIMO systems.

Sonars and ultrasound sensors

DAA was implemented in a big lot of sonars and medical ultrasound sensors.