Array signal processing and systems
Multidim Syst Sign Process
Array signal processing and systems
Bin Liao 0 1 2
Arjuna Madanayake 0 1 2
Panajotis Agathoklis 0 1 2
0 Department of Electrical and Computer Engineering, University of Victoria , Victoria , Canada
1 Department of Electrical and Computer Engineering, The University of Akron , Akron, OH , USA
2 College of Information Engineering, Shenzhen University , Shenzhen 518060 , China
-
Published online: 15 February 2018
© Springer Science+Business Media, LLC, part of Springer Nature 2018
We are delighted to present this special issue of Multidimensional Systems and Signal
Processing on Array Signal Processing and Systems. Sensor arrays play an important role in
spatio-temporal signal processing with applications spanning across multiple fields such as
electromagnetic, acoustics, ultrasonic and seismic processing systems. This plethora of
possible applications has sparked a large number of new theoretical developments and array
processing systems in the last few years.
In the field of electromagnetic sensor (antenna) arrays, there is much interest in the
electronically-steerable radio-frequency (RF) beams that can be obtained from phased-array
antennas. In the microwave bands, phased-array antennas take both analog and digital
signal processing approaches, and find applications in wireless base-stations, radar sensors,
space communications, and radio telescopes. In the emerging mm-wave regime, modern
applications of array processing are of paramount importance for wireless communications.
In particular, emerging 5G systems based on massive-MIMO basestations depend on array
processing to mitigate the effects of high path loss and blockages in an urban environment.
Acoustic sensor (microphone) arrays are extremely useful for audio and multimedia
applications, including high-fidelity sound recording, immersive multimedia and augmented/virtual
reality. Ultrasound sensor arrays find extensive applications in biomedical engineering where
they are used for imaging the human body and in structural health monitoring of airframes
and other structures in harsh environments. This Special Issue aims to present recent
important developments of array signal processing and systems. Both original research articles and
review articles in relevant fields are covered.
Following an open call for papers, seventeen articles are included in this Special Issue.
More precisely, the Special Issue includes one review article and sixteen research articles.
In the review article by
Romanofsky and Toonen (2016)
, a brief history of developments
which led to the realization of array antennas based on ferroelectric thin films is presented
and key performance differences provided by competing thin film deposition techniques are
highlighted. Moreover, the authors discuss the outlook of the impact that voltage-controlled
magnetism and magnetoelasticity (provided by emerging multiferroic thin films) will have
on future array antenna technologies.
The remaining 16 articles can be generally grouped into three thematic areas: (1) parameter
estimation using sensor arrays; (2) beamforming for sensor arrays, and (3) signal processing
in phased-array and multiple-input multiple-output (MIMO) radars as detailed below.
1 Parameter estimation using sensor arrays
In this group, we have 9 papers, which mainly focus on the estimation of
direction-ofarrival (DOA) and frequency. The paper by
Liu et al. (2016
) presented an algorithm for
two-dimensional DOA estimation of noncircular sources using an L-shaped sparse array
composed of two co-prime arrays. In this method, the array aperture can be significantly
increased, while the computational complexity is still acceptable since the azimuth angles
can be estimated without peak searching and eigenvalue decomposition. Simulation results
show that this method can provide improved performance in terms of the estimation accuracy
and resolution.
Ali Khan et al. (2016
) proposed a novel DOA estimation algorithm that uses the
adaptive directional t–f distribution (ADTFD) for the analysis of close signal components. This
algorithm optimizes the direction of kernel at each point in the t–f domain to obtain a clear
t–f representation, which is then exploited for DOA estimation. Experimental results
indicate the use of adaptive directional TFD outperforms other TFDs in terms of resolution and
cross-term suppression properties. This method also gives good results for sparse signals.
In the paper by
Zhang et al. (2017)
, a new method is presented to effectively estimate the
signal DOA and the phase error of a uniform linear array. Assuming that one sensor has been
calibrated, this method appropriately reconstructs the data matrix and establishes a series of
linear equations with respect to the unknown parameters through eigenvalue decomposition.
The unknown parameters can be determined directly by the least squares method. Unlike the
conventional methods, the proposed method only requires (...truncated)