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Electronics
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Recent Advances and Applications of Array Signal Processing
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Recent Advances and Applications of Array Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Microwave and Wireless Communications".

Deadline for manuscript submissions: closed (30 September 2023) | Viewed by 8445

Special Issue Editors

Dr. Xiangrong Wang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
Interests: array signal processing; radar signal processing; MIMO radar; antenna arrays; direction-of-arrival estimation
Dr. Renli Zhang

E-Mail Website
Guest Editor
Department of Communication, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: array signal processing; CFAR detection; anti-jamming; radar signal processing
Dr. Qisong Wu

E-Mail Website
Guest Editor
Key Laboratory of Underwater Acoustic Signal Processing of Ministry of Education, Southeast University, Nanjing 210096, China
Interests: array signal processing; Bayesian learning; underwater acoustic signal processing; radar signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensing with a single antenna cannot provide the wealth of information for the processing of signals in modern-day applications. Thus, multi-antenna arrays find extensive use in different sensing modalities, such as radars, telescopes, sonar, acoustic and ultrasound, etc. Sensing can be divided into active and passive sensing. Active sensing is achieved by transmitting probing signals and measuring the backscattered waveforms. Passive sensing, on the other hand, fulfils its mission using signals of opportunity. Regardless of whether active or passive sensing is used, both the antenna array configuration and the employed algorithms affect the underlying sensing functionality.

Most existing array applications employ a fixed and uniform array configuration, and focus on developing various kinds of beamforming algorithms. Beamforming is fundamental in numerous array processing applications. Whether for direction-of-arrival (DOA) estimation, interference suppression, or target detection/tracking/classification, both in radar and mm-wave communications, beamforming plays an essential role in the performance of subsequent signal processing steps. There are many approaches to beamforming, ranging from simple Fourier-based techniques to adaptive subspace-based methods. Whereas subspace-based methods have dominated beamforming research in the last decades, there have been recent advances in data-adaptive, high-resolution beamforming techniques and machine-learning-based techniques. Therefore, beamforming continues to attract significant attention in the research community.

Although uniform arrays provide the full complement of information and are straightforward to deal with, they contain significant inherent redundancy, allowing for reductions in their size, weight, and power (SWaP). Sparse sensor arrays are under-sampled such that several sensors are removed from their uniform counterparts, thus ensuring SWaP savings. Sparse arrays are established as state-of-the-art approaches for achieving high-resolution beamforming. The key to sparse arrays is the optimal placement of a given number of sensors along the physical aperture to satisfy specific criteria. Different objectives lead to differing sparse array configurations. From the perspective of DOA estimation, structured sparse arrays, such as nested arrays and coprime arrays, are easy to construct by following a deterministic formula to place antennas and enable the estimation of more sources than physical antennas. Unstructured sparse arrays that optimize antenna placement under the criterion of the maximum output signal-to-interference-plus-noise ratio (SINR) based on the knowledge learned from the sensed environment exhibit situational awareness and objective pertinence.

Driven by the importance of array signal processing, research into array signal processing techniques and applications continues unabated. This Special Issue brings together a number of contributions on recent advances and applications of array signal processing.

The main aim of this Special Issue is to seek high-quality submissions that highlight emerging developments and applications of array signal processing and algorithms, addressing the most recent breakthroughs.

Topics of interest include, but are not limited to:

  • Antenna array synthesis;
  • Adaptive beamforming;
  • Active arrays;
  • Passive arrays;
  • Array calibration;
  • Sparse arrays;
  • Arrays for autonomous driving;
  • Arrays for millimeter-wave communications;
  • Reconfigurable arrays;
  • Antenna array measurements;
  • Antenna array diagnosis;
  • Antenna array optimization;
  • Arrays for satellite communications;
  • Arrays for radar applications;
  • Arrays for imaging applications;
  • Arrays for astronomical applications;
  • Arrays for biomedical applications;
  • Arrays for underwater applications;
  • MIMO and Massive MIMO arrays;
  • Networks of small arrays;
  • Beamforming techniques;
  • Target detection;
  • Target tracking;
  • Target classification;
  • DOA and parameter estimation.

Dr. Xiangrong Wang
Dr. Renli Zhang
Dr. Qisong Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • beamforming
  • sparse arrays
  • array applications
  • array signal processing

Published Papers (6 papers)

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19 pages, 72288 KiB  
Article
Vehicle Detection Based on Information Fusion of mmWave Radar and Monocular Vision
by Guizhong Cai, Xianpeng Wang, Jinmei Shi, Xiang Lan, Ting Su and Yuehao Guo
Electronics 2023, 12(13), 2840; https://doi.org/10.3390/electronics12132840 - 27 Jun 2023
Viewed by 1394
Abstract
Single sensors often fail to meet the needs of practical applications due to their lack of robustness and poor detection accuracy in harsh weather and complex environments. A vehicle detection method based on the fusion of millimeter wave (mmWave) radar and monocular vision [...] Read more.
Single sensors often fail to meet the needs of practical applications due to their lack of robustness and poor detection accuracy in harsh weather and complex environments. A vehicle detection method based on the fusion of millimeter wave (mmWave) radar and monocular vision was proposed to solve this problem in this paper. The method successfully combines the benefits of mmWave radar for measuring distance and speed with the vision for classifying objects. Firstly, the raw point cloud data of mmWave radar can be processed by the proposed data pre-processing algorithm to obtain 3D detection points with higher confidence. Next, the density-based spatial clustering of applications with noise (DBSCAN) clustering fusion algorithm and the nearest neighbor algorithm were also used to correlate the same frame data and adjacent frame data, respectively. Then, the effective targets from mmWave radar and vision were matched under temporal-spatio alignment. In addition, the successfully matched targets were output by using the Kalman weighted fusion algorithm. Targets that were not successfully matched were marked as new targets for tracking and handled in a valid cycle. Finally, experiments demonstrated that the proposed method can improve target localization and detection accuracy, reduce missed detection occurrences, and efficiently fuse the data from the two sensors. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Array Signal Processing)
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15 pages, 1018 KiB  
Article
A Joint Angle and Frequency Spectrum Estimation Algorithm Using Difference Coarray
by Dan Li, Yanan Ma, Guangteng Fan and Yaowen Fu
Electronics 2023, 12(8), 1886; https://doi.org/10.3390/electronics12081886 - 17 Apr 2023
Viewed by 989
Abstract
The spectrum sensing that jointly estimates direction-of-arrival (DOA) and frequency spectrum is an important issue for cognitive radio. Existing off-grid DOA estimation algorithms using difference coarray require a large number of snapshots to guarantee the estimation accuracy. Meanwhile, inaccurate DOA estimation renders inaccurate [...] Read more.
The spectrum sensing that jointly estimates direction-of-arrival (DOA) and frequency spectrum is an important issue for cognitive radio. Existing off-grid DOA estimation algorithms using difference coarray require a large number of snapshots to guarantee the estimation accuracy. Meanwhile, inaccurate DOA estimation renders inaccurate frequency spectrum estimation due to coupled estimation process. In order to overcome these disadvantages, we propose a joint angle and frequency spectrum estimation algorithm using difference coarray in this work. The proposed algorithm first transforms the received signal into the coarray domain and then adopts an “estimate and subtract” method to separate multiple signals. Subsequently, the DOA estimate of each source is corrected iteratively based on the proposed simple interpolation method. Finally, the frequency spectra are obtained by estimating the frequency response matrix based on the previous estimated DOAs. The shortage of requiring a large number of snapshots are overcome by utilizing the deterministically orthogonal signal model in which the signals are completely uncorrelated. Simulation results demonstrate the effectiveness of the proposed algorithm for off-grid DOA and frequency spectrum estimation using difference coarray with few snapshots. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Array Signal Processing)
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18 pages, 5386 KiB  
Article
An Ultra-Wideband Plane Wave Generator for 5G Base Station Antenna Measurement
by Xinzhong Li, Litao Chen, Zhengpeng Wang, Ke Yang and Jungang Miao
Electronics 2023, 12(8), 1824; https://doi.org/10.3390/electronics12081824 - 12 Apr 2023
Cited by 3 | Viewed by 1568
Abstract
Plane-wave generators (PWGs) for over-the-air testing of 5G base stations offer the advantages of efficiency and economy. Many new bands, such as n28, are progressively being introduced, driving the bandwidth improvement of PWGs. The cost of amplitude–phase control networks is also increased by [...] Read more.
Plane-wave generators (PWGs) for over-the-air testing of 5G base stations offer the advantages of efficiency and economy. Many new bands, such as n28, are progressively being introduced, driving the bandwidth improvement of PWGs. The cost of amplitude–phase control networks is also increased by the broadband range required for testing. In view of the above challenges, in this paper, a low-frequency ultra-wideband PWG for testing 5G base stations is reported. Firstly, an electrically small antenna unit based on the Vivaldi antenna is design for the PWG. The antenna unit has a wide operating band and compact size, allowing it to reach a quarter of the minimum frequency wavelength. Then, the operating band from 700 MHz to 4 GHz is divided into three sub-bands, and the amplitude and phase excitations within each sub-band are optimized with multiple frequency points. Finally, the designed ultra-wideband PWG is simulated and experimentally tested. The designed 2.64 m one-dimensional linear-array PWG is able to produce a 1.5 m × 1.32 m quiet zone with less than 1.0 dB and 10°. The results of the radiation pattern measurements for the base station agree reasonably well with the MVG SG128. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Array Signal Processing)
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13 pages, 5381 KiB  
Article
Angular Super-Resolution of Multi-Channel APAR in Interference Environments
by Rui Liu, Jindong Zhang, Xiaobo Deng, Daiyin Zhu, Huangrong Zhou and Mingming Guo
Electronics 2023, 12(2), 392; https://doi.org/10.3390/electronics12020392 - 12 Jan 2023
Viewed by 1134
Abstract
Aiming to resolve azimuth-dense targets in interference environments, the radar needs to have the ability of single snap echo angular super-resolution with anti-interference. To solve the problem, the angular super-resolution algorithm based on single snap echo while anti-interference with blocking matrix method is [...] Read more.
Aiming to resolve azimuth-dense targets in interference environments, the radar needs to have the ability of single snap echo angular super-resolution with anti-interference. To solve the problem, the angular super-resolution algorithm based on single snap echo while anti-interference with blocking matrix method is studied for active phased array radar (APAR) in this paper. Since the super-resolution ability of the conventional MUSIC algorithm and iterative adaptive algorithm (IAA) algorithm are limited in single snap echo, the iterative re-weighted least squares (IRLS) algorithm under p-norm constraint is proposed. Further, the near main lobe interference suppression ability is enhanced by the adaptive diagonal loading method. The performance differences of IAA and IRLS algorithms for single-target detection and double-target angular super-resolution are analyzed in detail by numerical simulation on three scenes of no interference, side-lobe interference, and near-main-lobe interference. The simulation results show that the proposed algorithm can effectively solve the problem of target angle estimation and super-resolution based on single sample echo in an interference environment, including near main-lobe interference. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Array Signal Processing)
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14 pages, 2651 KiB  
Article
k-Level Extended Sparse Array Design for Direction-of-Arrival Estimation
by Pinjiao Zhao, Qisong Wu, Na Wu, Guobing Hu and Liwei Wang
Electronics 2022, 11(23), 3911; https://doi.org/10.3390/electronics11233911 - 26 Nov 2022
Viewed by 1185
Abstract
Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray [...] Read more.
Sparse arrays based on the concept of a sum-difference coarray (SDCA) have increased degrees of freedom and enlarged effective array aperture compared to those only considering a difference coarray. Nevertheless, there still exist a number of overlapping virtual sensors between the difference coarray and the sum coarray, yielding high coarray redundancy. In this paper, we propose a k-level extended sparse array configuration consisting of one sparse subarray with k-level expansion and one uniform linear subarray. By systematically analyzing the inherent structure of the k-level extended sparse array, the closed-form expressions for sensor locations, uniform DOF and coarray redundancy ratio (CARR) are derived. Moreover, with the utilization of a k-level extended strategy, the proposed array remains a hole-free property and achieves low coarray redundancy. According to the proposed sparse array, the spatial and temporal information of the incident sources are jointly exploited for underdetermined direction-of-arrival estimation. The theoretical propositions are proven and numerical simulations are performed to demonstrate the superior performance of the proposed array. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Array Signal Processing)
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12 pages, 2697 KiB  
Article
Oblique Projection-Based Covariance Matrix Reconstruction and Steering Vector Estimation for Robust Adaptive Beamforming
by Yanliang Duan, Yanping Gong, Xiaohui Yang and Weiping Cao
Electronics 2022, 11(21), 3478; https://doi.org/10.3390/electronics11213478 - 26 Oct 2022
Cited by 2 | Viewed by 1092
Abstract
Adaptive beamforming can efficiently contract interference and noise. Due to high sensitivity of the beamformer to model mismatch, the capability of interference reduction will critically degrade when the signal model mismatch occurs, particularly when the sampling sequence contains the desired signal. For the [...] Read more.
Adaptive beamforming can efficiently contract interference and noise. Due to high sensitivity of the beamformer to model mismatch, the capability of interference reduction will critically degrade when the signal model mismatch occurs, particularly when the sampling sequence contains the desired signal. For the purpose of enhancing the robustness of beamformers to signal model mismatch, we propose a new robust adaptive beamforming (RAB) method. Firstly, the precise steering vector (SV) associating with the desired signal is estimated by employing the minimum norm of subspace projection (MNSP) approach. Secondly, the nominal interference SVs are estimated via the maximum entropy power spectrum. Subsequently, the corrected interference SVs and powers are obtained by oblique projection. Finally, the interference-plus-noise covariance matrix (INCM) is reconstructed, and the proposed RAB is obtained. Multiple simulations are carried out and demonstrate the robustness of the proposed RAB method. Full article
(This article belongs to the Special Issue Recent Advances and Applications of Array Signal Processing)
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