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Advances in Array Signal Processing: Methods and Applications
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Advances in Array Signal Processing: Methods and Applications

A Special Issue of Electronics (ISSN 2079-9292) belonging to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 October 2026 | Viewed by 3405

Editors

Dr. Aifei Liu

E-Mail Website
Guest Editor
School of AI and Advanced Computing, Xi'an Jiaotong-Liverpool University, Taicang 215400, China
Interests: array signal processing; deep neural networks; adaptive signal processing; radar signal processing; anomaly detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Shengguo Shi

E-Mail Website
Guest Editor
College of Underwater Acoustics, Harbin Engineering University, Harbin 150001, China
Interests: acoustic vector sensors; sparse array design; array signal processing; noise field measurement and analysis
Dr. Feng Chen

E-Mail
Guest Editor
School of Information and Communication, Guilin University of Electronic Technology, Guilin 541004, China
Interests: acoustic sensors; array signal processing; beamforming; direction of arrival estimation

Special Issue Information

Dear Colleagues,

This Special Issue focuses on advancing the theory, methodology, and practical applications of Array Signal Processing (ASP), aiming to bridge classical techniques, cutting-edge innovations, and deep learning-driven methods while addressing real-world challenges in complex signal environments.

The Issue welcomes original research, reviews, and case studies across applications (communication, radar, sonar, acoustic sensing) to foster cross-disciplinary dialog and accelerate the translation of ASP innovations into practical systems.

The topics of interest include, but are not limited to, the following:

  • Deep learning-based ASP: Exploring how neural networks (fully connected deep neural networks (FC-DNNs), convolutional deep neural networks (CNNs), Transformers, graph neural networks) and data-driven frameworks can improve ASP performance—for example, end-to-end DOA estimation without prior signal models, adaptive beamforming for dynamic interference, or array fault diagnosis via anomaly detection. On the other hand, in order to deploy deep learning-based methods on the embedded systems, lightweight versions of the deep learning-based ASP methods are also appreciated.
  • Traditional ASP technologies such as beamforming, direction-of-arrival (DOA) estimation (MUSIC, ESPRIT), and array calibration, which remain foundational but require optimization for modern scenarios (e.g., low signal-to-noise ratio, non-stationary interference).
  • Advanced ASP methods such as sparse representation methods, sparse array design, and distributed array processing, which overcome classical array limitations (e.g., aperture constraints, spatial aliasing) to enhance spatial resolution and adaptability.
  • New applications of ASP: Non-contact respiratory and heartbeat detection using millimeter-wave radar, autonomous driving, and intelligent transportation fields.

We look forward to receiving your contributions.

Dr. Aifei Liu
Prof. Dr. Shengguo Shi
Dr. Feng Chen
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • array signal processing (ASP)
  • direction-of-arrival (DOA) estimation
  • array calibration
  • sparse array design
  • compressive sensing
  • adaptive beamforming
  • deep learning for signal processing
  • distributed array processing
  • neural network-based ASP (CNN, Transformer, GNN)
  • millimeter-wave radar

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Published Papers (5 papers)

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Research

26 pages, 1063 KB  
Article
Retrodirective Cross-Eye Jamming Recognition and Angle Estimation via Directional Modulation and Multiple-Signal Classification
by Heguo Huang, Tiancheng Lv, Renli Zhang and Weixing Sheng
Electronics 2026, 15(18), 4094; https://doi.org/10.3390/electronics15184094 - 10 Sep 2026
Abstract
This paper proposes a retrodirective cross-eye jamming (RCJ) recognition and angle estimation algorithm based on directional modulation and multiple signal classification (DM-MUSIC). Because RCJ intercepts the radar transmit waveform to produce a monopulse angle measurement result that deviates from the true target, the [...] Read more.
This paper proposes a retrodirective cross-eye jamming (RCJ) recognition and angle estimation algorithm based on directional modulation and multiple signal classification (DM-MUSIC). Because RCJ intercepts the radar transmit waveform to produce a monopulse angle measurement result that deviates from the true target, the traditional phased-array (TPA) radar that radiates identical transmit waveforms across the spatial domain fails to recognize RCJ by calculating the normalized cross-correlation function (NCCF). In DM-MUSIC, the monopulse angle measurement result induced by RCJ is derived, and the transmit phase matrix synthesis criterion in digital array radar is then formulated to minimize the NCCFs between the transmit waveform for the detection direction and RCJ-induced monopulse angle deception directions by utilizing the flexibility of DM in the waveform domain. Sequential quadratic programming combined with the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm is employed to calculate the transmit phase matrix. The RCJ system is then recognized by comparing the NCCFs of the received jamming signal associated with the DM transmit waveform in the detection direction and RCJ-induced monopulse angle deception directions. Finally, the synthesized DM transmit waveform and forward–backward spatial smoothing are used to decorrelate the jamming signals, and the RCJ angle is estimated by MUSIC. The simulation results demonstrate that DM-MUSIC achieves high recognition probability and accurate RCJ angle estimation. The recognition probability reaches 98.1% at a jamming-to-noise ratio (JNR) of 5dB, with an amplitude gain of 1 and a phase shift of 179°. At a JNR of 20dB, with an amplitude gain of 0.97 and a phase shift of 179°, the Root Mean Square Error of angle estimation result is reduced from 0.13° for FBSS-MUSIC to 0.063° for DM-MUSIC. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing: Methods and Applications)
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25 pages, 5271 KB  
Article
A Low-Complexity DOA Estimation Method for Acoustic Vector Sensors Based on Noise Power Invariance
by Yanzhou Feng and Feng Chen
Electronics 2026, 15(14), 3076; https://doi.org/10.3390/electronics15143076 - 13 Jul 2026
Viewed by 288
Abstract
To reduce the computational burden of conventional spectral search direction-of-arrival (DOA) estimation algorithms for acoustic vector sensor arrays (AVSAs), this paper proposes a low-complexity DOA estimation method based on semi-real-valued noise power invariance (SR-NPI). The proposed method is developed for centrosymmetric AVSAs under [...] Read more.
To reduce the computational burden of conventional spectral search direction-of-arrival (DOA) estimation algorithms for acoustic vector sensor arrays (AVSAs), this paper proposes a low-complexity DOA estimation method based on semi-real-valued noise power invariance (SR-NPI). The proposed method is developed for centrosymmetric AVSAs under the required steering-vector parity and pressure-channel conjugate-symmetry conditions after pressure–velocity (PV) co-processing. The AVSA measurements are first preprocessed through PV co-processing, and a pseudo-data covariance matrix is then reconstructed by exploiting the complex conjugate relationship between the true DOAs and their symmetric virtual DOAs. By introducing a scanning source into the reconstructed covariance matrix, a DOA-dependent spatial spectrum is constructed according to the eigenvalue-ordering behavior. Since the reconstructed matrix contains both the true DOAs and the symmetric virtual DOAs, the angular search range can be reduced to one half of the original domain. Under the tested configuration, the proposed method reduces the computational cost to approximately 1.92% of that of the original NPI algorithm. Simulation and sea trial results indicate that SR-NPI can maintain competitive estimation accuracy while significantly reducing computational complexity under the centrosymmetric-array conditions. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing: Methods and Applications)
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30 pages, 19341 KB  
Article
Computationally Efficient Deep Learning Approach Using IQ-MobNet for Radar DoA Estimation in Limited Snapshot Conditions
by Neeraja P. Kovilakam, Bindiya T. Sambasivan and Raghu C. Variyam
Electronics 2026, 15(13), 2956; https://doi.org/10.3390/electronics15132956 - 6 Jul 2026
Viewed by 410
Abstract
This paper presents a computationally efficient deep learning framework for accurate direction-of-arrival (DoA) estimation in portable radar applications. Leveraging a MobileNet architecture, the proposed model directly processes raw in-phase and quadrature-phase (IQ) data, enabling more effective learning of both spatial and temporal features. [...] Read more.
This paper presents a computationally efficient deep learning framework for accurate direction-of-arrival (DoA) estimation in portable radar applications. Leveraging a MobileNet architecture, the proposed model directly processes raw in-phase and quadrature-phase (IQ) data, enabling more effective learning of both spatial and temporal features. This direct input approach enhances DoA estimation accuracy, particularly under challenging conditions such as low signal-to-noise ratio (SNR) and limited snapshot scenarios. A unified training strategy is adopted for both single-source and multi-source target detection, ensuring consistency and robustness. Comprehensive simulation experiments demonstrate the proposed model’s competitive and robust performance across various conditions, including different SNR levels, closely spaced targets, and random off-grid angles. It also shows that our method achieves performance comparable to or better than recent deep learning approaches in several challenging scenarios, establishing its potential for resource-constrained environments where only low snapshot data are available. The proposed IQ-MobNet DoA estimation model achieves this competitive performance with substantially lower computational complexity, requiring only 0.24 million parameters and 0.42 million Floating Point Operations (FLOPs), representing a reduction of over 96% compared to the recent neural network models. To ensure practical applicability, the proposed IQ-MobNet framework is validated using real-world measured data, confirming its robustness beyond simulated environments. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing: Methods and Applications)
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25 pages, 1664 KB  
Article
A Joint Optimization Method for Radio Antenna Arrays Under Tri-Domain Errors and Atmospheric Effects Based on Improved Dueling DQN
by Xiaotian Wang, Liang Dong, Xuebao Li, Yanfang Zheng, Hongwei Ye, Shunhang Zhang, Yongshang Lv and Honglei Jin
Electronics 2026, 15(13), 2808; https://doi.org/10.3390/electronics15132808 - 25 Jun 2026
Viewed by 277
Abstract
This paper presents a co-optimization framework for sparse concentric ring arrays based on an improved Dueling Deep Q-Network (DDQN) with a two-tier adaptive step-size strategy. The method aims at joint sidelobe suppression and structural optimization under non-ideal conditions. A tri-domain stochastic error model [...] Read more.
This paper presents a co-optimization framework for sparse concentric ring arrays based on an improved Dueling Deep Q-Network (DDQN) with a two-tier adaptive step-size strategy. The method aims at joint sidelobe suppression and structural optimization under non-ideal conditions. A tri-domain stochastic error model is introduced to characterize position, phase, and amplitude perturbations, and atmospheric-effect-aware evaluation is incorporated for high-frequency propagation scenarios. For a six-ring sparse array, radius-only optimization achieves a PSLL of −24.4810 dB, corresponding to an average improvement of 5.809 dB over the initial array and an additional reduction compared with the baseline DDQN method. Extending the design to joint optimization of ring radii and element counts further reduces the PSLL to −30.629 dB, demonstrating the effectiveness of combined geometric and sparsity control. Monte Carlo simulations show that the optimized array maintains stable sidelobe performance under tri-domain stochastic perturbations, with an average PSLL of −26.758 dB. Further analysis using real meteorological data indicates that atmospheric effects introduce moderate variations in the normalized beam pattern, while the overall performance remains primarily influenced by stochastic perturbations under the considered modeling conditions. The proposed framework provides an effective and robust optimization approach for sparse concentric ring arrays in practical high-frequency scenarios. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing: Methods and Applications)
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16 pages, 1925 KB  
Article
Coprime Distributed Array for Super-Resolution DOA Estimation
by Ming Guo, Tingting Ma, Zixuan Shen, Zewei Liu, Yuee Zhou, Shenghui Li and Jian Wang
Electronics 2025, 14(23), 4562; https://doi.org/10.3390/electronics14234562 - 21 Nov 2025
Viewed by 1348
Abstract
The increasing complexity of the electromagnetic environment, driven by rapid advancements in communication and radar technologies, places greater demands on direction of arrival (DOA) estimation. While traditional antenna arrays improve performance by increasing the number of elements, this approach raises hardware costs and [...] Read more.
The increasing complexity of the electromagnetic environment, driven by rapid advancements in communication and radar technologies, places greater demands on direction of arrival (DOA) estimation. While traditional antenna arrays improve performance by increasing the number of elements, this approach raises hardware costs and design complexity with reducing system flexibility. Distributed arrays offer a promising alternative by enhancing angular accuracy and resolution without additional elements. However, conventional uniformly distributed radars suffer from high hardware costs and computational complexity. To overcome this issue, this paper proposes a distributed radar architecture based on a coprime arrangement. By deploying two subarrays with coprime spacings, the proposed structure significantly reduces hardware requirements while maintaining high angle estimation accuracy. Simulations validate the effectiveness of the proposed configuration. Under the conditions of a signal-to-noise ratio of 0 dB and 50 snapshots, the angle measurement error reached (10−3)°. Full article
(This article belongs to the Special Issue Advances in Array Signal Processing: Methods and Applications)
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