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Advances in Array Signal Processing

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Circuit and Signal Processing".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 11854

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Special Issue Editors

Dr. Yangyang Dong

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Guest Editor

School of Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: array signal processing; source localization; spectrum sensing

Dr. Hua Chen

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Guest Editor

Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
Interests: array signal processing; MIMO radar; passive localization
Special Issues, Collections and Topics in MDPI journals

Dr. Fangqing Wen

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Guest Editor

Electronic and Communication Institute, China Three Gorges University, Yichang 443002, China
Interests: array signal processing; wireless sensor network; MIMO radar
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue (SI) is entitled “Advances in Array Signal Processing”. Array signal processing plays a critical role in every area where spatial information is obtained or utilized, such as radar, sonar, communications, astronomy, seismology, etc. Generally, beamforming and direction of arrival (DOA) estimation are two main tasks of array signal processing. In terms of beamforming, we can control the array beampattern to acquire the desired signal via spatial filter, which is beneficial for long-range radar, high-quality communications, etc. With DOA estimation, engineers can obtain accurate angles to determine the position of various targets. Traditional array signal processing has focused on general scenarios, and there is a limit to the degree to which its performance can be improved, as it is restricted by model mismatch. In recent years, with the developments in compressed sensing, convex or nonconvex optimization, multilinear algebra (including tensor and quaternion) and deep learning, more information related to the array, scenario and targets is utilized, and better performance is achieved. Undeniably, array signal processing is a strong and vibrant subject deserving exploration at academic frontiers, and can be used to solve engineering problems related to systems based on multi-sensor arrays. We look forward to the latest research on array signal processing in terms of algorithms and applications. Authors are encouraged to submit contributions in any of the following or related areas:

Sparse array signal processing;
Array signal processing with array errors;
Array signal processing with impulsive noise;
Multilinear-algebra-based array signal processing;
Deep-learning-based array signal processing;
Array signal processing with low-bits sampling.

Dr. Yangyang Dong
Dr. Hua Chen
Dr. Fangqing Wen
Guest Editors

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Keywords

array signal processing
impulsive noise
array errors
multilinear algebra
deep learning

Published Papers (11 papers)

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Research

19 pages, 713 KiB

Open AccessFeature PaperArticle

Exploiting Time–Frequency Sparsity for Dual-Sensor Blind Source Separation

by Jiajia Chen, Haijian Zhang and Siyu Sun

Electronics 2024, 13(7), 1227; https://doi.org/10.3390/electronics13071227 - 26 Mar 2024

Viewed by 255

Abstract

This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e.,

M = 2

, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and [...] Read more.

This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e.,

M = 2

, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than M. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct

N > 2

sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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24 pages, 3917 KiB

Open AccessArticle

Efficient 2D DOA Estimation via Decoupled Projected Atomic Norm Minimization

by Mingming Liu, Yangyang Dong, Chunxi Dong and Guoqing Zhao

Electronics 2024, 13(5), 846; https://doi.org/10.3390/electronics13050846 - 22 Feb 2024

Viewed by 495

Abstract

This paper presents an efficient two-dimensional (2D) direction of arrival (DOA) estimation method, termed as decoupled projected atomic norm minimization (D-PANM), to solve the angle-ambiguity problem. It first introduces a novel atomic metric via projecting the original atom set onto a smoothing space, based on which we formulate an equivalent semi-definite programming (SDP) problem. Then, two relatively low-complexity decoupled Toeplitz matrices can be obtained to estimate the DOAs. We further exploit the structural information hidden in the newly constructed data to avoid pair matching for the azimuth and elevation angles when the number of sensors is odd, and then propose a fast and feasible decoupled alternating projections (D-AP) algorithm, reducing computational complexity to a great extent. Numerical simulations are performed to demonstrate that the proposed algorithm is no longer restricted by angle ambiguity scenarios, but instead provides a more stable estimation performance, even when multiple signals share the same angles in both azimuth and elevation dimensions. Additionally, it greatly improves the resolution, with control of the computation load compared with the existing atomic norm minimization (ANM) algorithm. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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20 pages, 6407 KiB

Open AccessArticle

A Novel Complex-Valued Blind Source Separation and Its Applications in Integrated Reception

by Weilin Luo, Hongbin Jin, Xiaobai Li, Hao Li, Kang Liu and Ruijuan Yang

Electronics 2023, 12(18), 3954; https://doi.org/10.3390/electronics12183954 - 20 Sep 2023

Cited by 1 | Viewed by 756

Abstract

The separation of time–frequency mixing signals composed of radar, communication, and jamming is the first step in integrated reception processing, which requires higher accuracy for complex blind source separation (CVBSS). However, traditional CVBSS methods have limitations such as low separation accuracy, a slow convergence speed, and poor robustness in low signal-to-noise ratio (SNR) and high jamming-to-signal ratio (JSR) scenarios. To address the above issues, this paper firstly establishes a time delay mixing mathematical model. A robust whitening algorithm is proposed by using the time delay correlation matrix of the observed signal, which is insensitive to noise. Secondly, the joint diagonalized F-parametrization is used as the objective function, and the separation matrix is constructed based on the multiple complex-valued Givens matrices. The complex-valued Givens matrix not only ensures orthogonality in the separation matrix but also effectively reduces the number of parameters to be calculated. This approach guarantees accuracy and simplifies the complexity of the separation process. Finally, the nonlinear chaotic grey wolf optimizer is utilized to search for the optimal rotation angle. The simulation results demonstrate that this algorithm offers higher separation accuracy and requires fewer iterations compared to the traditional algorithm. Additionally, it enhances the accuracy of direction of arrival (DOA) estimation, reduces the communication bit error rate, and enables the joint estimation of the target distance and velocity even in the presence of powerful jamming and a low SNR. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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17 pages, 3162 KiB

Open AccessArticle

A Novel Low-Complexity Method for Near-Field Sources Based on an S-IMISC Array Model

by Xiaolin Li, Hongjuan Yang, Jiqu Han and Ningfei Dong

Electronics 2023, 12(11), 2435; https://doi.org/10.3390/electronics12112435 - 27 May 2023

Viewed by 845

Abstract

Array optimization has recently received significant attention owing to its several advantages, such as larger array aperture and greater degrees of freedom (DOFs). However, current works focus on far-field sources, while array optimization for near-field sources has not been adequately investigated. Therefore, this work develops a new symmetry sparse array model for near-field sources based on the improved maximum inter-element spacing constraint (IMISC). The proposed symmetry IMISC (S-IMISC) array model has all the advantages of traditional sparse array models. Compared with traditional sparse array models, the S-IMISC array model affords more uniform DOFs and is less affected by mutual coupling. Additionally, in order to improve the real-time performance of near-field sources localization, the characteristic equation-based method (CEM) is used to obtain the azimuth information of near-field sources which can avoid eigenvalue decomposition (EVD), and a spectrum peak search and compression scheme is used to obtain the distance information by searching the partial area instead of the whole Fresnel area, thereby significantly reducing computation complexity. Extensive simulations verify the advantages of the proposed algorithm and the S-IMISC array model. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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14 pages, 3370 KiB

Open AccessArticle

Mixed Near-Field and Far-Field Sources Localization via Oblique Projection

by Heping Shi, Yanjie Yang, Guanghui Yan and Shaohua Wang

Electronics 2023, 12(10), 2340; https://doi.org/10.3390/electronics12102340 - 22 May 2023

Viewed by 999

Abstract

This paper presents a novel mixed source localization algorithm based on high-order cumulant (HOC) and oblique projection techniques. To address the issue of lower accuracy in near-field source (NFS) localization compared to the far-field source (FFS) localization, the presented algorithm further enhances the accuracy of NFS localization. First, the FFS’s direction-of-arrival (DOA) estimate is acquired utilizing a multiple signal classification (MUSIC) spectral peak search. To classify mixed sources more effectively, we utilize the oblique projection technique, which can successfully prevent FFS information from influencing the estimation of NFS parameters. A HOC matrix with solely NFS DOA information is built by choosing array elements in a specific sequence. The estimation of the NFS DOA is then derived using the estimation of signal parameters via a rotational invariance technique (ESPRIT)-like algorithm. Finally, the NFS range is acquired by a MUSIC search. The performance of the presented algorithm is discussed in several aspects. Compared to existing matrix difference methods, the presented algorithm, which adopts the oblique projection method, achieves superior results in the separation of mixed sources. Without excessively increasing the computational complexity, it not only ensures the performance of localization parameter estimation for FFS but also estimates the NFS with higher precision. The numerical simulations attest to the superior performance of the presented algorithm. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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10 pages, 424 KiB

Open AccessCommunication

Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields

by Ming-Yan Gong and Bin Lyu

Electronics 2023, 12(10), 2191; https://doi.org/10.3390/electronics12102191 - 11 May 2023

Viewed by 952

Abstract

The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of the expectation–maximization algorithm, for solving the ML direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas, and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm that the ECME algorithm can efficiently obtain the ML based DOA estimate of each stochastic source. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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13 pages, 2522 KiB

Open AccessArticle

Hybrid T-Shaped Sensor Array Composed of Acoustic Vector Sensors and Scalar Sensors

by Wei Rao, Yuanqing Li and Dan Li

Electronics 2023, 12(8), 1813; https://doi.org/10.3390/electronics12081813 - 11 Apr 2023

Viewed by 965

Abstract

Through the more available acoustic information or the polarization information provided, vector sensor arrays outperform the scalar sensor arrays in accuracy of localization. However, the cost of a vector sensor array is higher than that of a scalar sensor array. To reduce the cost of a two-dimensional (2-D) vector sensor array, a hybrid T-shaped sensor array consisting of two orthogonal uniform linear arrays (ULAs) is proposed, where one ULA is composed of acoustic vector sensors and the other is composed of scalar sensors. By utilizing the cross-correlation tensor between the received signals from the two ULAs, two virtual uniform rectangular arrays (URAs) of acoustic vector sensors are obtained, and they can be combined into a larger URA. It is shown that a larger acoustic vector sensor URA with M²+1 degrees of freedom (DOFs) can be obtained from the specially designed T-shaped array with M acoustic vector sensors and 2M scalar sensors. Furthermore, by means of the proposed tensor model for the larger URA, the inter-sensor spacing can be allowed to exceed greatly a half-wavelength. Accordingly, the proposed method can achieve both a high DOF and a large array aperture. Simulation results show that the proposed method has a better performance in 2-D direction-of-arrival estimation than some existing methods under the same array cost. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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16 pages, 4047 KiB

Open AccessArticle

Wideband Direction-of-Arrival Estimation Based on Hierarchical Sparse Bayesian Learning for Signals with the Same or Different Frequency Bands

by Yixin Yang, Yahao Zhang, Long Yang and Yong Wang

Electronics 2023, 12(5), 1123; https://doi.org/10.3390/electronics12051123 - 25 Feb 2023

Cited by 2 | Viewed by 827

Abstract

Wideband sparse Bayesian learning (WSBL) based on joint sparsity achieves high direction-of-arrival (DOA) estimation precision when the signals share the same frequency band. However, when the signal frequency bands are non-overlapped or partially overlapped, i.e., the frequency bands are different, the performance of the method degrades due to the improper prior on signal. This paper aims at extending the WSBL to a more general version, which is also suitable for the cases where the signal frequency bands are non-overlapped or partially overlapped. Given that the signals are sparsely distributed in the space, the signal matrix whose column is composed of the signal in each frequency bin is row-sparse. Moreover, the signal vectors in some frequency bins have different sparse supports when the signals occupy the different frequency bands. Therefore, a hierarchical sparse prior is assigned to the signal matrix, where a set of hyperparameters are used to ensure the row-sparsity and the other set are used to adjust the signal sparsity in each frequency bin. The DOAs are finally estimated in the Bayesian framework. The simulation results verify that the proposed method achieves good performance on estimation precision in both the same and different frequency band scenarios. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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14 pages, 2993 KiB

Open AccessArticle

A Target Detection Method of Distributed Passive Radar without Direct-Path Signal

by Huijie Zhu, Changlong Wang and Lu Wang

Electronics 2023, 12(2), 433; https://doi.org/10.3390/electronics12020433 - 13 Jan 2023

Cited by 6 | Viewed by 1865

Abstract

At present, there are many technical methods in the field of target detection, but the detection methods are greatly affected by direct-path signals and need technical support such as extracting pure direct-path signals, so they cannot be used under the condition of without-direct-path signals. In this paper, a distributed target detection method is studied under a without-direct-path signals system. In the case of without-direct-path signals, target detection is achieved by the generalized likelihood ratio test (GLRT). At the same time, the input echo signals in target detection need time synchronization, so the impact of time delay should be eliminated. However, the traditional time delay estimation method is realized through coherent processing between echoes, which requires a high signal-to-noise ratio (SNR). Therefore, a time delay estimation method is proposed in this paper. Finally, the experimental results show that the accuracy of target detection by GLRT is improved, and the signal-to-noise ratio is also improved under the condition of without-direct-path signals. Moreover, the accuracy of delay detection is improved. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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11 pages, 911 KiB

Open AccessFeature PaperArticle

Fast Heterogeneous Clutter Suppression Method Based on Improved Sparse Bayesian Learning

by Qiang Wang, Yani Zhang, Zhihui Li and Weihu Zhao

Electronics 2023, 12(2), 343; https://doi.org/10.3390/electronics12020343 - 09 Jan 2023

Viewed by 1427

Abstract

In order to deal with the problem space-time adaptive processing (STAP) performance degradation of an airborne phased array system caused by the serious shortage of independent and identical distributed (IID) training samples in the nonhomogeneous clutter environment, an improved direct data domain method based on sparse Bayesian learning is proposed in this paper, which only uses a single snapshot data of a cell under test (CUT) to suppress the clutter and has fast computational speed. Firstly, three hyper-parameters required to obtain the sparse solution are derived. Secondly, the comparative analysis of their iterative formulas is made, and the piecewise iteration of hyper-parameter that has an obvious influence on the computational complexity of obtaining sparse solution is presented. Lastly, with the approximate prior information of the target, the clutter sparse solution is given and its covariance matrix is effectively estimated to calculate the adaptive filter weight and realize the clutter suppression. Simulation results verify that the proposal can dramatically decrease the computational burden while keeping the superior heterogeneous clutter suppression performance. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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13 pages, 2485 KiB

Open AccessArticle

Interrupted-Sampling and Non-Uniform Periodic Repeater Jamming against mDT-STAP System

by Jiyang Li, Xiaohu Duan, Jia Li and Peng Bai

Electronics 2023, 12(1), 152; https://doi.org/10.3390/electronics12010152 - 29 Dec 2022

Cited by 3 | Viewed by 980

Abstract

The difference between sampling data and detection data can degrade the performance of space-time adaptive processing (STAP). A jamming algorithm with a non-uniform periodic repeater based on interrupted-sampling is proposed against the reduced dimensional space-time adaptive processing (STAP) system for the first time. Firstly, the model of m-bins doppler transform (mDT) STAP training and processing signal samples is described. Then, the method of false targets generated by the non-uniform periodic repeater is analyzed theoretically based on the principle of interrupted-sampling. The simulation shows that numerous false targets with different amplitude and intervals can be generated by changing the retransmitted parameters. The independent identical distribution (IID) of system sample data can be destroyed after these false targets are received by the radar system, and the main lobe will be distorted when the system’s adaptive weight vector is formed. The processing performance of the mDT-STAP system is seriously degraded. The jamming method proposed based on interrupted-sampling and the non-uniform periodic repeater offers great potential for the interference research on STAP in real conditions. Full article

(This article belongs to the Special Issue Advances in Array Signal Processing)

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