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Advances in Sparse Sensor Arrays
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Advances in Sparse Sensor Arrays

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Communications".

Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 9623

Special Issue Editor

Prof. Dr. Xiaofei Zhang

E-Mail Website
Guest Editor
College of Electronic Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: array signal processing; MIMO radar; communication signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sparse arrays, such as coprime and nested arrays, have recently attracted considerable attention for their application in improving active and passive sensing in radar, navigation, underwater acoustics and wireless communications. Sparse array signal processing provides a systematical framework for sparse sampling and array structure with enlarged aperture, enhanced spatial resolution, increased degrees of freedom (DOFs) and reduced mutual coupling. Difference-co-array-based approaches, e.g., spatial smoothing technique based algorithms, Toeplitz-property-based algorithms and sparse reconstruction methods, can circumvent spatial aliasing and offer unique a response to targets with sparse sampling in time, space and frequency. Temporal and spatial sparse samplings encounter merits in direction of arrival (DOA) estimation and adaptive beamforming.

Potential topics include but are not limited to the following:

  • Generalizations of co-prime and nested arrays for increased DOFs
  • Array geometry optimization for high-accuracy DOA estimation
  • Sparse array calibration and mutual coupling effect
  • Convex and nonconvex optimizations related to array signal processing
  • Off-grid and grid-less solutions to super-resolution
  • Sparse-recovery-based methods for DOA estimation
  • Robust DOA estimation in low SNR or small snapshot number
  • Multi-dimensional sparse array signal processing
  • Hardware implementation and design
  • Applications to sonar, radar, MRI, geolocation, and other areas

Prof. Dr. Xiaofei Zhang
Guest Editor

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. Sensors 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 2600 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

  • sensor array
  • DOA estimation
  • sparse sensor array
  • array signal processing

Published Papers (5 papers)

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Research

14 pages, 5763 KiB  
Article
Direction-of-Arrival Estimation Based on Frequency Difference–Wavenumber Analysis for Sparse Vertical Array Configuration
by Donghyeon Kim, Gihoon Byun and Jeasoo Kim
Sensors 2023, 23(1), 337; https://doi.org/10.3390/s23010337 - 28 Dec 2022
Cited by 1 | Viewed by 1563
Abstract
Frequency–wavenumber (f–k) analysis can estimate the direction of arrival (DOA) of broadband signals received on a vertical array. When the vertical array configuration is sparse, it results in an aliasing error due to spatial sampling; thus, several striation patterns [...] Read more.
Frequency–wavenumber (f–k) analysis can estimate the direction of arrival (DOA) of broadband signals received on a vertical array. When the vertical array configuration is sparse, it results in an aliasing error due to spatial sampling; thus, several striation patterns can emerge in the f–k domain. This paper extends the f–k analysis to a sparse receiver-array, wherein a multitude of sidelobes prevent resolving the DOA estimates due to spatial aliasing. The frequency difference-wavenumber (Δf–k) analysis is developed by adopting the concept of frequency difference, and demonstrated its performance of DOA estimation to a sparse receiver array. Experimental results verify the robustness of the proposed Δf–k analysis in the estimation of the DOA of cracking sounds generated by the snapping shrimps, which were recorded by a sparse vertical array configuration during the shallow water experiment. Full article
(This article belongs to the Special Issue Advances in Sparse Sensor Arrays)
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22 pages, 9796 KiB  
Article
A Comparison of Surrogate Behavioral Models for Power Amplifier Linearization under High Sparse Data
by Jose Alejandro Galaviz-Aguilar, Cesar Vargas-Rosales, José Ricardo Cárdenas-Valdez, Daniel Santiago Aguila-Torres and Leonardo Flores-Hernández
Sensors 2022, 22(19), 7461; https://doi.org/10.3390/s22197461 - 1 Oct 2022
Cited by 1 | Viewed by 1519
Abstract
A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and [...] Read more.
A good approximation to power amplifier (PA) behavioral modeling requires precise baseband models to mitigate nonlinearities. Since digital predistortion (DPD) is used to provide the PA linearization, a framework is necessary to validate the modeling figures of merit support under signal conditioning and transmission restrictions. A field-programmable gate array (FPGA)-based testbed is developed to measure the wide-band PA behavior using a single-carrier 64-quadrature amplitude modulation (QAM) multiplexed by orthogonal frequency-division multiplexing (OFDM) based on long-term evolution (LTE) as a stimulus, with different bandwidths signals. In the search to provide a heuristic target approach modeling, this paper introduces a feature extraction concept to find an appropriate complexity solution considering the high sparse data issue in amplitude to amplitude (AM-AM) and amplitude to phase AM-PM models extraction, whose penalties are associated with overfitting and hardware complexity in resulting functions. Thus, experimental results highlight the model performance for a high sparse data regime and are compared with a regression tree (RT), random forest (RF), and cubic-spline (CS) model accuracy capabilities for the signal conditioning to show a reliable validation, low-complexity, according to the peak-to-average power ratio (PAPR), complementary cumulative distribution function (CCDF), coefficients extraction, normalized mean square error (NMSE), and execution time figures of merit. The presented models provide a comparison with original data that aid to compare the dimension and robustness for each surrogate model where (i) machine learning (ML)-based and (ii) CS interpolate-based where high sparse data are present, NMSE between the CS interpolated based are also compared to demonstrate the efficacy in the prediction methods with lower convergence times and complexities. Full article
(This article belongs to the Special Issue Advances in Sparse Sensor Arrays)
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16 pages, 2158 KiB  
Article
Real-Valued Direct Position Determination of Quasi-Stationary Signals for Nested Arrays: Khatri–Rao Subspace and Unitary Transformation
by Haowei Zeng, Heng Yue, Jinke Cao and Xiaofei Zhang
Sensors 2022, 22(11), 4209; https://doi.org/10.3390/s22114209 - 31 May 2022
Cited by 1 | Viewed by 1585
Abstract
The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal’s covariance for different frames and further [...] Read more.
The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal’s covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri–Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results. Full article
(This article belongs to the Special Issue Advances in Sparse Sensor Arrays)
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20 pages, 2523 KiB  
Article
Unfolded Coprime Linear Array with Three Subarrays for Non-Gaussian Signals: Configuration Design and DOA Estimation
by Meng Yang, Jingming Li, Changbo Ye and Jianfeng Li
Sensors 2022, 22(4), 1339; https://doi.org/10.3390/s22041339 - 10 Feb 2022
Cited by 1 | Viewed by 1441
Abstract
In this paper, we investigate the problem of sparse array design for the direction of the arrival (DOA) of non-Gaussian signals and exploit the unfolded coprime linear array with three subarrays (UCLATS) to obtain physical sensors location. With the motivation from the large [...] Read more.
In this paper, we investigate the problem of sparse array design for the direction of the arrival (DOA) of non-Gaussian signals and exploit the unfolded coprime linear array with three subarrays (UCLATS) to obtain physical sensors location. With the motivation from the large consecutive degree of freedom (DOF), we optimize the process of obtaining physical sensors location from two steps. Specifically, the first is to model the process of obtaining the longest consecutive virtual sum co-array from a given number of physical array elements into a global postage-stamp problem (GPSP), whose solution can be employed to determine the locations of the longest possible consecutive sum co-array (2-SC) and initial physical array. The second step is to multiply the location of the virtual sum co-array by appropriate coprime coefficients to generate UCLATS and then multiply the initial physical array position by the same corresponding coefficients to obtain physical sensors location. Besides, an algorithm is proposed to obtain DOA estimates, which employs the discrete Fourier transform (DFT) method and partial spectrum searching multiple signal classification (PSS-MUSIC) algorithm to obtain initial estimates and fine estimates, respectively, termed as the DFT-MUSIC method. Compared with the traditional total spectrum searching MUSIC (TSS-MUSIC) algorithm, the DFT-MUSIC method performs the same asymptotical performance of DOA estimation with less than 10% complex multiplication times, which can be verified by numerical simulations under the same condition. Full article
(This article belongs to the Special Issue Advances in Sparse Sensor Arrays)
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17 pages, 3052 KiB  
Article
Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array
by Pan Gong and Xixin Chen
Sensors 2022, 22(1), 136; https://doi.org/10.3390/s22010136 - 25 Dec 2021
Cited by 3 | Viewed by 2484
Abstract
In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal [...] Read more.
In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms. Full article
(This article belongs to the Special Issue Advances in Sparse Sensor Arrays)
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