Digital antenna array(DAA) is a smart antenna with multi channels digital beamforming, usually by using fast Fourier transform (FFT). The development and practical realization of digital antenna arrays theory started in 1962 under the guidance of Vladimir Varyukhin (USSR).
The history of the DAA was started to emerge as a theory of multichannel analysis in the 1920s. [1] In the 1940s this theory evolved to the theory of three-channel antenna analyzers. [1]
The implementation of effective signal processing in radars by the end of the 1950s predetermined the use of electronic computers in this field. In 1957, Ben S. Meltont and Leslie F. Bailey published article [2] regarding using algebraic operations for signal processing with the help of electronic circuits or analog computer. [1]
Three years after in 1960 the idea of using high-speed computers to solve directional finding problems was embodied, initially to locate earthquake epicenter. B. A. Bolt was one of the first who implemented this idea in practice. [1] [3] Almost simultaneously a similar approach was used by Flinn, a research fellow of the Australian National University. [4]
Despite the fact that in the mentioned experiments the interaction between sensors and computers was implemented with the help of data input cards, such decision was a decisive step on the way of the appearance of the DAA. Then, it was needed only to solve the problem of direct digital data input into the computer from sensors, excluding the step of preparation of punch card and operator assistance as a surplus element. [1] This step for radars theory was made after 1962 in the former USSR conducted with a solution to the problem of superRayleigh resolution of the emission sources. [1]
The main approach to digital signal processing in DAA is the "digital beamforming" after Analog-to-digital converters (ADC) of receiver channels or before Digital-to-analog converters (DAC) by transmission.
Digital beamforming of DAA has advantages because the digital signals can be transformed and combined in parallel, to produce 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. [6]
Before digital beamforming operation should be used a correction of channels characteristics by a special test source or using the heterodyne signal. [7] [8] [9] Such correction can be used not only for receiving channels but also in transmission channels of active DAA. [10]
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 in ADCs and DACs. [11] [12]
In maximum likelihood beamformer (DML), the noise is modeled as a stationary Gaussian white random processes while the signal waveform as deterministic (but arbitrary) and unknown.
The Bartlett beamformer is a natural extension of conventional spectral analysis (spectrogram) 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, also known as the minimum-variance distortionless response (MVDR) beamforming algorithm, [13] has a power given by
.
The MVDR/Capon beamformer can achieve better resolution than the conventional (Bartlett) 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. [14]
MUSIC (MUltiple SIgnal Classification) 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.
The important trend in evolving digital signal processing for DAA is the use of Artificial Intelligence technologies. [15]
DAA use to improve the performance of radio communications in MIMO [10] (Massive MIMO) systems.
DAA was implemented in a big lot of sonars and medical ultrasound sensors. [14]
In electronics and telecommunications, jitter is the deviation from true periodicity of a presumably periodic signal, often in relation to a reference clock signal. In clock recovery applications it is called timing jitter. Jitter is a significant, and usually undesired, factor in the design of almost all communications links.
In antenna theory, a phased array usually means an electronically scanned array, a computer-controlled array of antennas which creates a beam of radio waves that can be electronically steered to point in different directions without moving the antennas. The general theory of an electromagnetic phased array also finds applications in ultrasonic and medical imaging application and in optics optical phased array.
Synthetic-aperture radar (SAR) is a form of radar that is used to create two-dimensional images or three-dimensional reconstructions of objects, such as landscapes. SAR uses the motion of the radar antenna over a target region to provide finer spatial resolution than conventional stationary beam-scanning radars. SAR is typically mounted on a moving platform, such as an aircraft or spacecraft, and has its origins in an advanced form of side looking airborne radar (SLAR). The distance the SAR device travels over a target during the period when the target scene is illuminated creates the large synthetic antenna aperture. Typically, the larger the aperture, the higher the image resolution will be, regardless of whether the aperture is physical or synthetic – this allows SAR to create high-resolution images with comparatively small physical antennas. For a fixed antenna size and orientation, objects which are further away remain illuminated longer – therefore SAR has the property of creating larger synthetic apertures for more distant objects, which results in a consistent spatial resolution over a range of viewing distances.
A sensor array is a group of sensors, usually deployed in a certain geometry pattern, used for collecting and processing electromagnetic or acoustic signals. The advantage of using a sensor array over using a single sensor lies in the fact that an array adds new dimensions to the observation, helping to estimate more parameters and improve the estimation performance. For example an array of radio antenna elements used for beamforming can increase antenna gain in the direction of the signal while decreasing the gain in other directions, i.e., increasing signal-to-noise ratio (SNR) by amplifying the signal coherently. Another example of sensor array application is to estimate the direction of arrival of impinging electromagnetic waves. The related processing method is called array signal processing. A third examples includes chemical sensor arrays, which utilize multiple chemical sensors for fingerprint detection in complex mixtures or sensing environments. Application examples of array signal processing include radar/sonar, wireless communications, seismology, machine condition monitoring, astronomical observations fault diagnosis, etc.
Array processing is a wide area of research in the field of signal processing that extends from the simplest form of 1 dimensional line arrays to 2 and 3 dimensional array geometries. Array structure can be defined as a set of sensors that are spatially separated, e.g. radio antenna and seismic arrays. The sensors used for a specific problem may vary widely, for example microphones, accelerometers and telescopes. However, many similarities exist, the most fundamental of which may be an assumption of wave propagation. Wave propagation means there is a systemic relationship between the signal received on spatially separated sensors. By creating a physical model of the wave propagation, or in machine learning applications a training data set, the relationships between the signals received on spatially separated sensors can be leveraged for many applications.
Beamforming or spatial filtering is a signal processing technique used in sensor arrays for directional signal transmission or reception. This is achieved by combining elements in an antenna array in such a way that signals at particular angles experience constructive interference while others experience destructive interference. Beamforming can be used at both the transmitting and receiving ends in order to achieve spatial selectivity. The improvement compared with omnidirectional reception/transmission is known as the directivity of the array.
In signal processing, direction of arrival (DOA) denotes the direction from which usually a propagating wave arrives at a point, where usually a set of sensors are located. These set of sensors forms what is called a sensor array. Often there is the associated technique of beamforming which is estimating the signal from a given direction. Various engineering problems addressed in the associated literature are:
Smart antennas are antenna arrays with smart signal processing algorithms used to identify spatial signal signatures such as the direction of arrival (DOA) of the signal, and use them to calculate beamforming vectors which are used to track and locate the antenna beam on the mobile/target. Smart antennas should not be confused with reconfigurable antennas, which have similar capabilities but are single element antennas and not antenna arrays.
The angle of arrival (AoA) of a signal is the direction from which the signal is received.
An adaptive beamformer is a system that performs adaptive spatial signal processing with an array of transmitters or receivers. The signals are combined in a manner which increases the signal strength to/from a chosen direction. Signals to/from other directions are combined in a benign or destructive manner, resulting in degradation of the signal to/from the undesired direction. This technique is used in both radio frequency and acoustic arrays, and provides for directional sensitivity without physically moving an array of receivers or transmitters.
In statistics, the generalized linear array model (GLAM) is used for analyzing data sets with array structures. It based on the generalized linear model with the design matrix written as a Kronecker product.
In radio, multiple-input and multiple-output (MIMO) is a method for multiplying the capacity of a radio link using multiple transmission and receiving antennas to exploit multipath propagation. MIMO has become an essential element of wireless communication standards including IEEE 802.11n, IEEE 802.11ac, HSPA+ (3G), WiMAX, and Long Term Evolution (LTE). More recently, MIMO has been applied to power-line communication for three-wire installations as part of the ITU G.hn standard and of the HomePlug AV2 specification.
In mathematics, the Johnson–Lindenstrauss lemma is a result named after William B. Johnson and Joram Lindenstrauss concerning low-distortion embeddings of points from high-dimensional into low-dimensional Euclidean space. The lemma states that a set of points in a high-dimensional space can be embedded into a space of much lower dimension in such a way that distances between the points are nearly preserved. In the classical proof of the lemma, the embedding is a random orthogonal projection.
The first smart antennas were developed for military communications and intelligence gathering. The growth of cellular telephone in the 1980s attracted interest in commercial applications. The upgrade to digital radio technology in the mobile phone, indoor wireless network, and satellite broadcasting industries created new opportunities for smart antennas in the 1990s, culminating in the development of the MIMO technology used in 4G wireless networks.
Sonar systems are generally used underwater for range finding and detection. Active sonar emits an acoustic signal, or pulse of sound, into the water. The sound bounces off the target object and returns an “echo” to the sonar transducer. Unlike active sonar, passive sonar does not emit its own signal, which is an advantage for military vessels. But passive sonar cannot measure the range of an object unless it is used in conjunction with other passive listening devices. Multiple passive sonar devices must be used for triangulation of a sound source. No matter whether active sonar or passive sonar, the information included in the reflected signal can not be used without technical signal processing. To extract the useful information from the mixed signal, some steps are taken to transfer the raw acoustic data.
Beamforming is a signal processing technique used to spatially select propagating waves. In order to implement beamforming on digital hardware the received signals need to be discretized. This introduces quantization error, perturbing the array pattern. For this reason, the sample rate must be generally much greater than the Nyquist rate.
High Resolution Wide Swath (HRWS) imaging is an important branch in synthetic aperture radar (SAR) imaging, a remote sensing technique capable of providing high resolution images independent of weather conditions and sunlight illumination. This makes SAR very attractive for the systematic observation of dynamic processes on the Earth's surface, which is useful for environmental monitoring, earth resource mapping and military systems.
Multiple-input multiple-output (MIMO) radar is an extension of a traditional radar system to utilize multiple-inputs and multiple-outputs (antennas), similar to MIMO techniques used to increase the capacity of a radio link. MIMO radar is an advanced type of phased array radar employing digital receivers and waveform generators distributed across the aperture. MIMO radar signals propagate in a fashion similar to multistatic radar. However, instead of distributing the radar elements throughout the surveillance area, antennas are closely located to obtain better spatial resolution, Doppler resolution, and dynamic range. MIMO radar may also be used to obtain low-probability-of-intercept radar properties.
Vladimir Alekseevich Varyukhin was a Soviet and Ukrainian scientist, Professor, Doctor of Technical Sciences, Honored Scientist of the Ukrainian SSR, Major-General, founder of the theory of multichannel analysis, and creator of the scientific school on digital antenna arrays (DAAs).
Vadym Slyusar – Soviet and Ukrainian scientist, Professor, Doctor of Technical Sciences, Honored Scientist and Technician of Ukraine, founder of tensor-matrix theory of digital antenna arrays (DAAs), N-OFDM and other theories in fields of radar systems, smart antennas for wireless communications and digital beamforming.
This article incorporates textby Vadym Slyusar available under the CC BY-SA 3.0 license.The text and its release have been received by the Wikimedia Volunteer Response Team .
