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Special Issue "Recent Advances in Array Signal Processing and Its Applications in IoT Security"

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

Deadline for manuscript submissions: 30 September 2017

Special Issue Editors

Guest Editor
Dr. Zhiguo Shi

College of Information and Electronic Engineering, Zhejiang University, China
Website | E-Mail
Interests: array signal processing; positioning and target tracking; mobile crowd-sensing; data acquisition in IoT; secure and privacy in IoT; signal processing for communications
Guest Editor
Dr. Yujie Gu

Department of Electrical and Computer Engineering, Temple University, Philadelphia, USA
E-Mail
Interests: radar signal processing; statistical and array signal processing; wireless communications; compressive sensing and sparse reconstruction; optimization techniques
Guest Editor
Dr. Rongxing Lu

Faculty of Computer Science, University of New Brunswick, Canada
Website | E-Mail
Interests: wireless network security; cloud and fog computing security; big data security and privacy; secure and privacy in IoT; secure opportunistic computing; secure social network; applied cryptography

Special Issue Information

Dear Colleagues,

In past few years, there were several exciting breakthroughs in the field of array signal processing, which are promising to expand its applications in radar, sonar, wireless communications, acoustics, seismology, medical imaging, and radio astronomy. Specifically, nested array and coprime array provide a systematic framework for sparse sampling and array configuration with increased degrees-of-freedom (DOFs). The gridless sparse methods for direction-of-arrival (DOA) estimation stimulate the new research interests by solving an atomic/nuclear norm minimization problem. The adaptive beamformer can obtain the near optimal output performance by reconstructing the interference covariance matrix. Nevertheless, there are still many fundamental theoretical and technical challenges for the practical array application, such as (a) the upper bound of DOFs for any arbitrary number of sensors by optimizing the array geometry; (b) more robust and efficient array calibration methods which will achieve better final performance; (c) more computationally efficient gridless sparse methods for DOA estimation by exploiting the continuous compressive sensing. Hence, there is a pressing demand for developing innovative and efficacious signal processing algorithms for future array configuration and system implementation. In addition, the Internet of Things (IoT), as an emerging technique, has brought us many opportunities nowadays. However, more connected devices in IoT mean more attack vectors and more possibilities for adversaries to target us, IoT security challenges therefore cannot be ignored. Although many research efforts have been put on IoT security today, existing traditional security techniques, e.g., cryptographic encryption and authentication, are insufficient to solve all IoT security challenges, as IoT devices in unattended scenarios are easily compromised. Because array signal processing has its advantages in source detection and localization, it might be used for intrusion detection and location based IoT devices authentication. Therefore, there is a huge research potential of array signal processing in IoT security.

The goal of the Special Issue is to publish the most recent research results in array signal processing and its application in IoT security. Review papers on this topic are also welcome. Topics of interest in this Special Issue include, but are not limited to:

  • Coprime array signal processing
  • Sparse array design
  • Source enumeration and DOA estimation
  • MIMO array and massive array
  • Adaptive beamforming
  • Array calibration and decoupling
  • Space-time adaptive processing (STAP)
  • Tensor modeling and processing
  • Array applications to radar, sonar, microphone, wireless communications
  • Array applications in security detection in IoT
  • Array applications in location-based authentication in IoT

Dr. Zhiguo Shi
Dr. Yujie Gu
Dr. Rongxing Lu
Guest Editors

Manuscript Submission Information

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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 monthly 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 1800 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
  • Sparse array design
  • Source enumeration
  • DOA estimation
  • Adaptive beamforming
  • STAP
  • Tensor modeling and processing
  • Array applications in IoT security

Published Papers (24 papers)

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Research

Open AccessArticle A Direct Coarray Interpolation Approach for Direction Finding
Sensors 2017, 17(9), 2149; doi:10.3390/s17092149 (registering DOI)
Received: 7 August 2017 / Revised: 7 September 2017 / Accepted: 14 September 2017 / Published: 19 September 2017
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Abstract
Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve O(MN) sources with only O(M+N) sensors, and is a
[...] Read more.
Sparse arrays have gained considerable attention in recent years because they can resolve more sources than the number of sensors. The coprime array can resolve O ( M N ) sources with only O ( M + N ) sensors, and is a popular sparse array structure due to its closed-form expressions for array configuration and the reduction of the mutual coupling effect. However, because of the existence of holes in its coarray, the performance of subspace-based direction of arrival (DOA) estimation algorithms such as MUSIC and ESPRIT is limited. Several coarray interpolation approaches have been proposed to address this issue. In this paper, a novel DOA estimation approach via direct coarray interpolation is proposed. By using the direct coarray interpolation, the reshaping and spatial smoothing operations in coarray-based DOA estimation are not needed. Compared with existing approaches, the proposed approach can achieve a better accuracy with lower complexity. In addition, an improved angular resolution capability is obtained by using the proposed approach. Numerical simulations are conducted to validate the effectiveness of the proposed approach. Full article
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Open AccessArticle Underdetermined Blind Source Separation of Synchronous Orthogonal Frequency Hopping Signals Based on Single Source Points Detection
Sensors 2017, 17(9), 2074; doi:10.3390/s17092074
Received: 14 July 2017 / Revised: 5 September 2017 / Accepted: 6 September 2017 / Published: 11 September 2017
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Abstract
This paper considers the complex-valued mixing matrix estimation and direction-of-arrival (DOA) estimation of synchronous orthogonal frequency hopping (FH) signals in the underdetermined blind source separation (UBSS). A novel mixing matrix estimation algorithm is proposed by detecting single source points (SSPs) where only one
[...] Read more.
This paper considers the complex-valued mixing matrix estimation and direction-of-arrival (DOA) estimation of synchronous orthogonal frequency hopping (FH) signals in the underdetermined blind source separation (UBSS). A novel mixing matrix estimation algorithm is proposed by detecting single source points (SSPs) where only one source contributes its power. Firstly, the proposed algorithm distinguishes the SSPs by the comparison of the normalized coefficients of time frequency (TF) points, which is more effective than existing detection algorithms. Then, mixing matrix of FH signals can be estimated by the hierarchical clustering method. To sort synchronous orthogonal FH signals, a modified subspace projection method is presented to obtain the DOAs of FH. One superiority of this paper is that the estimation accuracy of the mixing matrix can be significantly improved by the proposed SSPs detection criteria. Another superiority of this paper is that synchronous orthogonal FH signals can be sorted in underdetermined condition. The experimental results demonstrate the efficiency of the two proposed algorithms. Full article
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Open AccessArticle Position Fingerprint-Based Beam Selection in Millimeter Wave Heterogeneous Networks
Sensors 2017, 17(9), 2009; doi:10.3390/s17092009
Received: 14 July 2017 / Revised: 28 August 2017 / Accepted: 30 August 2017 / Published: 1 September 2017
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Abstract
The traditional beam selection algorithms determine the optimal beam direction by feeding back the perfect channel state information (CSI) in a millimeter wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) system. Popular beam selection algorithms mostly focus on the methods of feedback and exhaustive search.
[...] Read more.
The traditional beam selection algorithms determine the optimal beam direction by feeding back the perfect channel state information (CSI) in a millimeter wave (mmWave) massive Multiple-Input Multiple-Output (MIMO) system. Popular beam selection algorithms mostly focus on the methods of feedback and exhaustive search. In order to reduce the extra computational complexity coming from the redundant feedback and exhaustive search, a position fingerprint (PFP)-based mmWave multi-cell beam selection scheme is proposed in this paper. In the proposed scheme, the best beam identity (ID) and the strongest interference beam IDs from adjacent cells of each fingerprint spot are stored in a fingerprint database (FPDB), then the optimal beam and the strongest interference beams can be determined by matching the current PFP of the user equipment (UE) with the PFP in the FPDB instead of exhaustive search, and the orthogonal codes are also allocated to the optimal beam and the strongest interference beams. Simulation results show that the proposed PFP-based beam selection scheme can reduce the computational complexity and inter-cell interference and produce less feedback, and the system sum-rate for the mmWave heterogeneous networks is also improved. Full article
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Open AccessArticle Improved Spatial Differencing Scheme for 2-D DOA Estimation of Coherent Signals with Uniform Rectangular Arrays
Sensors 2017, 17(9), 1956; doi:10.3390/s17091956
Received: 3 August 2017 / Revised: 22 August 2017 / Accepted: 22 August 2017 / Published: 24 August 2017
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Abstract
This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data
[...] Read more.
This paper proposes an improved spatial differencing (ISD) scheme for two-dimensional direction of arrival (2-D DOA) estimation of coherent signals with uniform rectangular arrays (URAs). We first divide the URA into a number of row rectangular subarrays. Then, by extracting all the data information of each subarray, we only perform difference-operation on the auto-correlations, while the cross-correlations are kept unchanged. Using the reconstructed submatrices, both the forward only ISD (FO-ISD) and forward backward ISD (FB-ISD) methods are developed under the proposed scheme. Compared with the existing spatial smoothing techniques, the proposed scheme can use more data information of the sample covariance matrix and also suppress the effect of additive noise more effectively. Simulation results show that both FO-ISD and FB-ISD can improve the estimation performance largely as compared to the others, in white or colored noise conditions. Full article
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Open AccessArticle Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
Sensors 2017, 17(8), 1896; doi:10.3390/s17081896
Received: 1 July 2017 / Revised: 2 August 2017 / Accepted: 15 August 2017 / Published: 17 August 2017
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Abstract
In this paper, we study the emergence of topological interference alignment and the characterizing features of a multi-user broadcast interference relay channel. We propose an alternative transmission strategy named the relay space-time interference alignment (R-STIA) technique, in which a K-user multiple-input-multiple-output (MIMO)
[...] Read more.
In this paper, we study the emergence of topological interference alignment and the characterizing features of a multi-user broadcast interference relay channel. We propose an alternative transmission strategy named the relay space-time interference alignment (R-STIA) technique, in which a K -user multiple-input-multiple-output (MIMO) interference channel has massive antennas at the transmitter and relay. Severe interference from unknown transmitters affects the downlink relay network channel and degrades the system performance. An additional (unintended) receiver is introduced in the proposed R-STIA technique to overcome the above problem, since it has the ability to decode the desired signals for the intended receiver by considering cooperation between the receivers. The additional receiver also helps in recovering and reconstructing the interference signals with limited channel state information at the relay (CSIR). The Alamouti space-time transmission technique and minimum mean square error (MMSE) linear precoder are also used in the proposed scheme to detect the presence of interference signals. Numerical results show that the proposed R-STIA technique achieves a better performance in terms of the bit error rate (BER) and sum-rate compared to the existing broadcast channel schemes. Full article
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Open AccessArticle Direction-of-Arrival Estimation with Coarray ESPRIT for Coprime Array
Sensors 2017, 17(8), 1779; doi:10.3390/s17081779
Received: 17 July 2017 / Revised: 29 July 2017 / Accepted: 31 July 2017 / Published: 3 August 2017
PDF Full-text (1016 KB) | HTML Full-text | XML Full-text
Abstract
A coprime array is capable of achieving more degrees-of-freedom for direction-of-arrival (DOA) estimation than a uniform linear array when utilizing the same number of sensors. However, existing algorithms exploiting coprime array usually adopt predefined spatial sampling grids for optimization problem design or include
[...] Read more.
A coprime array is capable of achieving more degrees-of-freedom for direction-of-arrival (DOA) estimation than a uniform linear array when utilizing the same number of sensors. However, existing algorithms exploiting coprime array usually adopt predefined spatial sampling grids for optimization problem design or include spectrum peak search process for DOA estimation, resulting in the contradiction between estimation performance and computational complexity. To address this problem, we introduce the Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) to the coprime coarray domain, and propose a novel coarray ESPRIT-based DOA estimation algorithm to efficiently retrieve the off-grid DOAs. Specifically, the coprime coarray statistics are derived according to the received signals from a coprime array to ensure the degrees-of-freedom (DOF) superiority, where a pair of shift invariant uniform linear subarrays is extracted. The rotational invariance of the signal subspaces corresponding to the underlying subarrays is then investigated based on the coprime coarray covariance matrix, and the incorporation of ESPRIT in the coarray domain makes it feasible to formulate the closed-form solution for DOA estimation. Theoretical analyses and simulation results verify the efficiency and the effectiveness of the proposed DOA estimation algorithm. Full article
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Open AccessArticle Performance Analysis of the Direct Position Determination Method in the Presence of Array Model Errors
Sensors 2017, 17(7), 1550; doi:10.3390/s17071550
Received: 8 May 2017 / Revised: 22 June 2017 / Accepted: 29 June 2017 / Published: 2 July 2017
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Abstract
The direct position determination approach was recently presented as a promising technique for the localization of a transmitting source with accuracy higher than that of the conventional two-step localization method. In this paper, the theoretical performance of a direct position determination estimator proposed
[...] Read more.
The direct position determination approach was recently presented as a promising technique for the localization of a transmitting source with accuracy higher than that of the conventional two-step localization method. In this paper, the theoretical performance of a direct position determination estimator proposed by Weiss is examined for situations in which the array model errors are present. Our study starts from a matrix eigen-perturbation result, which expresses the perturbation of eigenvalues as a function of the disturbance added to the Hermitian matrix. The first-order asymptotic expression of the positioning errors is presented, from which an analytical expression for the mean square error of the direct localization is available. Additionally, explicit formulas for computing the probabilities of a successful localization are deduced. Finally, Cramér–Rao bound expressions for the position estimation are derived for two cases: (1) array model errors are absent and (2) array model errors are present. The obtained Cramér-Rao bounds provide insights into the effects of the array model errors on the localization accuracy. Simulation results support and corroborate the theoretical developments made in this paper. Full article
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Open AccessArticle Analysis of Maneuvering Targets with Complex Motions by Two-Dimensional Product Modified Lv’s Distribution for Quadratic Frequency Modulation Signals
Sensors 2017, 17(6), 1460; doi:10.3390/s17061460
Received: 10 April 2017 / Revised: 13 June 2017 / Accepted: 14 June 2017 / Published: 21 June 2017
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Abstract
For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate
[...] Read more.
For targets with complex motion, such as ships fluctuating with oceanic waves and high maneuvering airplanes, azimuth echo signals can be modeled as multicomponent quadratic frequency modulation (QFM) signals after migration compensation and phase adjustment. For the QFM signal model, the chirp rate (CR) and the quadratic chirp rate (QCR) are two important physical quantities, which need to be estimated. For multicomponent QFM signals, the cross terms create a challenge for detection, which needs to be addressed. In this paper, by employing a novel multi-scale parametric symmetric self-correlation function (PSSF) and modified scaled Fourier transform (mSFT), an effective parameter estimation algorithm is proposed—referred to as the Two-Dimensional product modified Lv’s distribution (2D-PMLVD)—for QFM signals. The 2D-PMLVD is simple and can be easily implemented by using fast Fourier transform (FFT) and complex multiplication. These measures are analyzed in the paper, including the principle, the cross term, anti-noise performance, and computational complexity. Compared to the other three representative methods, the 2D-PMLVD can achieve better anti-noise performance. The 2D-PMLVD, which is free of searching and has no identifiability problems, is more suitable for multicomponent situations. Through several simulations and analyses, the effectiveness of the proposed estimation algorithm is verified. Full article
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Open AccessArticle Dual-Channel Cosine Function Based ITD Estimation for Robust Speech Separation
Sensors 2017, 17(6), 1447; doi:10.3390/s17061447
Received: 11 April 2017 / Revised: 2 June 2017 / Accepted: 6 June 2017 / Published: 20 June 2017
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Abstract
In speech separation tasks, many separation methods have the limitation that the microphones are closely spaced, which means that these methods are unprevailing for phase wrap-around. In this paper, we present a novel speech separation scheme by using two microphones that does not
[...] Read more.
In speech separation tasks, many separation methods have the limitation that the microphones are closely spaced, which means that these methods are unprevailing for phase wrap-around. In this paper, we present a novel speech separation scheme by using two microphones that does not have this restriction. The technique utilizes the estimation of interaural time difference (ITD) statistics and binary time-frequency mask for the separation of mixed speech sources. The novelties of the paper consist in: (1) the extended application of delay-and-sum beamforming (DSB) and cosine function for ITD calculation; and (2) the clarification of the connection between ideal binary mask and DSB amplitude ratio. Our objective quality evaluation experiments demonstrate the effectiveness of the proposed method. Full article
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Open AccessArticle Two Novel Two-Stage Direction of Arrival Estimation Algorithms for Two-Dimensional Mixed Noncircular and Circular Sources
Sensors 2017, 17(6), 1433; doi:10.3390/s17061433
Received: 23 April 2017 / Revised: 15 June 2017 / Accepted: 16 June 2017 / Published: 18 June 2017
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Abstract
This paper addresses the two-dimensional (2D) direction-of-arrival (DOA) estimation problem with two novel methods for mixed noncircular and circular signals. The first proposed method is named the two-stage direction-of-arrival matrix (TSDOAM) method, and the other is called the two-stage rank reduction (TSRARE) method.
[...] Read more.
This paper addresses the two-dimensional (2D) direction-of-arrival (DOA) estimation problem with two novel methods for mixed noncircular and circular signals. The first proposed method is named the two-stage direction-of-arrival matrix (TSDOAM) method, and the other is called the two-stage rank reduction (TSRARE) method. The proposed methods utilize both the circularity and the direction-of-arrival differences between the noncircular and circular sources to estimate the 2D directions-of-arrival (DOAs). The maximum detectable 2D angle parameters of the TSDOAM and TSRARE methods are twice those of the existing methods. Moreover, the TSRARE method can detect more incident signals than the TSDOAM method due to the array aperture of two parallel uniform linear arrays (ULAs) being fully utilized. Simulation results show that compared to the existing methods for the small angle separation of 2D directions-of-arrival, the two proposed methods perform well in terms of the signal-to-noise ratio (SNR) and snapshots. Full article
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Open AccessArticle Two-Dimensional DOA Estimation for Coherently Distributed Sources with Symmetric Properties in Crossed Arrays
Sensors 2017, 17(6), 1300; doi:10.3390/s17061300
Received: 21 March 2017 / Revised: 1 June 2017 / Accepted: 5 June 2017 / Published: 6 June 2017
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Abstract
In this paper, a novel algorithm is proposed for the two-dimensional (2D) central direction-of-arrival (DOA) estimation of coherently distributed (CD) sources. Specifically, we focus on a centro-symmetric crossed array consisting of two uniform linear arrays (ULAs). Unlike the conventional low-complexity methods using the
[...] Read more.
In this paper, a novel algorithm is proposed for the two-dimensional (2D) central direction-of-arrival (DOA) estimation of coherently distributed (CD) sources. Specifically, we focus on a centro-symmetric crossed array consisting of two uniform linear arrays (ULAs). Unlike the conventional low-complexity methods using the one-order Taylor series approximation to obtain the approximate rotational invariance relation, we first prove the symmetric property of angular signal distributed weight vectors of the CD source for an arbitrary centrosymmetric array, and then use this property to establish two generalized rotational invariance relations inside the array manifolds in the two ULAs. Making use of such relations, the central elevation and azimuth DOAs are obtained by employing a polynomial-root-based search-free approach, respectively. Finally, simple parameter matching is accomplished by searching for the minimums of the cost function of the estimated 2D angular parameters. When compared with the existing low-complexity methods, the proposed algorithm can greatly improve estimation accuracy without significant increment in computation complexity. Moreover, it performs independently of the deterministic angular distributed function. Simulation results are presented to illustrate the performance of the proposed algorithm. Full article
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Open AccessArticle Computationally Efficient Direction Finding for a Mixture of Circular and Strictly Noncircular Sources with Uniform Rectangular Arrays
Sensors 2017, 17(6), 1269; doi:10.3390/s17061269
Received: 22 February 2017 / Revised: 26 May 2017 / Accepted: 27 May 2017 / Published: 2 June 2017
PDF Full-text (963 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm for the mixed circular and strictly noncircular sources is proposed. A general array model with a mixture of signals is firstly built based on uniform rectangular arrays (URAs), and then, the approach,
[...] Read more.
In this paper, a novel two-dimensional (2D) direction-of-arrival (DOA) estimation algorithm for the mixed circular and strictly noncircular sources is proposed. A general array model with a mixture of signals is firstly built based on uniform rectangular arrays (URAs), and then, the approach, which uses the rank-reduction-based ROOT-MUSIC, can solve 2D DOA estimation problem. Besides, the theoretical error of the proposed algorithm, a criterion of the performance for evaluation, is analyzed by the first-order Taylor expression using second-order statistics. As verified by the simulation results, a better DOA estimation performance and a lower computational complexity are achieved by the proposed algorithm than the existing methods resorting to the noncircularity of the incoming signals. Full article
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Open AccessArticle Research and Analysis on the Localization of a 3-D Single Source in Lossy Medium Using Uniform Circular Array
Sensors 2017, 17(6), 1274; doi:10.3390/s17061274
Received: 28 March 2017 / Revised: 19 May 2017 / Accepted: 23 May 2017 / Published: 2 June 2017
PDF Full-text (2125 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the methods and analysis for estimating the location of a three-dimensional (3-D) single source buried in lossy medium are presented with uniform circular array (UCA). The mathematical model of the signal in the lossy medium is proposed. Using information in
[...] Read more.
In this paper, the methods and analysis for estimating the location of a three-dimensional (3-D) single source buried in lossy medium are presented with uniform circular array (UCA). The mathematical model of the signal in the lossy medium is proposed. Using information in the covariance matrix obtained by the sensors’ outputs, equations of the source location (azimuth angle, elevation angle, and range) are obtained. Then, the phase and amplitude of the covariance matrix function are used to process the source localization in the lossy medium. By analyzing the characteristics of the proposed methods and the multiple signal classification (MUSIC) method, the computational complexity and the valid scope of these methods are given. From the results, whether the loss is known or not, we can choose the best method for processing the issues (localization in lossless medium or lossy medium). Full article
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Open AccessArticle DOA Finding with Support Vector Regression Based Forward–Backward Linear Prediction
Sensors 2017, 17(6), 1225; doi:10.3390/s17061225
Received: 19 April 2017 / Revised: 22 May 2017 / Accepted: 24 May 2017 / Published: 27 May 2017
PDF Full-text (1214 KB) | HTML Full-text | XML Full-text
Abstract
Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward–backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This
[...] Read more.
Direction-of-arrival (DOA) estimation has drawn considerable attention in array signal processing, particularly with coherent signals and a limited number of snapshots. Forward–backward linear prediction (FBLP) is able to directly deal with coherent signals. Support vector regression (SVR) is robust with small samples. This paper proposes the combination of the advantages of FBLP and SVR in the estimation of DOAs of coherent incoming signals with low snapshots. The performance of the proposed method is validated with numerical simulations in coherent scenarios, in terms of different angle separations, numbers of snapshots, and signal-to-noise ratios (SNRs). Simulation results show the effectiveness of the proposed method. Full article
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Open AccessArticle A Low-Complexity DOA and Polarization Method of Polarization-Sensitive Array
Sensors 2017, 17(5), 1170; doi:10.3390/s17051170
Received: 20 March 2017 / Revised: 3 May 2017 / Accepted: 12 May 2017 / Published: 20 May 2017
PDF Full-text (310 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a low-complexity method to estimate the direction of arrival and polarization based on the polarization sensitive array (PSA) which is composed of cross-dipoles. We built a half-quaternions model through the Cayley–Dickson form to remove the redundant information. Then, the directions
[...] Read more.
This paper proposes a low-complexity method to estimate the direction of arrival and polarization based on the polarization sensitive array (PSA) which is composed of cross-dipoles. We built a half-quaternions model through the Cayley–Dickson form to remove the redundant information. Then, the directions of arrival (DOAs) were estimated via the root-MUSIC algorithm. Finally, the polarizations were estimated by generalized eigenvalue method. Unlike some existing searching algorithms, such as multiple signal classification (MUSIC), this method can avoid the peak searching and maintains high estimation accuracy. Moreover, we use the oblique projection operators to filter out the interference signals which are decoys of the target signal. Simulation results demonstrate the effectiveness and favorable performance of the proposed method. Full article
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Open AccessArticle An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising
Sensors 2017, 17(5), 1140; doi:10.3390/s17051140
Received: 29 March 2017 / Revised: 4 May 2017 / Accepted: 12 May 2017 / Published: 16 May 2017
Cited by 2 | PDF Full-text (341 KB) | HTML Full-text | XML Full-text
Abstract
Co-prime arrays can estimate the directions of arrival (DOAs) of O(MN) sources with O(M+N) sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number
[...] Read more.
Co-prime arrays can estimate the directions of arrival (DOAs) of O ( M N ) sources with O ( M + N ) sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number of degrees of freedom is limited due to the existence of holes in difference coarrays if subspace-based algorithms such as the spatial smoothing multiple signal classification (MUSIC) algorithm are utilized. To address this issue, techniques such as positive definite Toeplitz completion and array interpolation have been proposed in the literature. Another factor that compromises the accuracy of DOA estimation is the limitation of the number of snapshots. Coarray-based processing is particularly sensitive to the discrepancy between the sample covariance matrix and the ideal covariance matrix due to the finite number of snapshots. In this paper, coarray interpolation based on matrix completion (MC) followed by a denoising operation is proposed to detect more sources with a higher accuracy. The effectiveness of the proposed method is based on the capability of MC to fill in holes in the virtual sensors and that of MC denoising operation to reduce the perturbation in the sample covariance matrix. The results of numerical simulations verify the superiority of the proposed approach. Full article
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Open AccessArticle Low-Cost Nested-MIMO Array for Large-Scale Wireless Sensor Applications
Sensors 2017, 17(5), 1105; doi:10.3390/s17051105
Received: 25 March 2017 / Revised: 7 May 2017 / Accepted: 9 May 2017 / Published: 12 May 2017
PDF Full-text (473 KB) | HTML Full-text | XML Full-text
Abstract
In modern communication and radar applications, large-scale sensor arrays have increasingly been used to improve the performance of a system. However, the hardware cost and circuit power consumption scale linearly with the number of sensors, which makes the whole system expensive and power-hungry.
[...] Read more.
In modern communication and radar applications, large-scale sensor arrays have increasingly been used to improve the performance of a system. However, the hardware cost and circuit power consumption scale linearly with the number of sensors, which makes the whole system expensive and power-hungry. This paper presents a low-cost nested multiple-input multiple-output (MIMO) array, which is capable of providing O ( 2 N 2 ) degrees of freedom (DOF) with O ( N ) physical sensors. The sensor locations of the proposed array have closed-form expressions. Thus, the aperture size and number of DOF can be predicted as a function of the total number of sensors. Additionally, with the help of time-sequence-phase-weighting (TSPW) technology, only one receiver channel is required for sampling the signals received by all of the sensors, which is conducive to reducing the hardware cost and power consumption. Numerical simulation results demonstrate the effectiveness and superiority of the proposed array. Full article
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Open AccessArticle Ambiguity Resolution for Phase-Based 3-D Source Localization under Fixed Uniform Circular Array
Sensors 2017, 17(5), 1086; doi:10.3390/s17051086
Received: 17 March 2017 / Revised: 18 April 2017 / Accepted: 3 May 2017 / Published: 11 May 2017
PDF Full-text (3931 KB) | HTML Full-text | XML Full-text
Abstract
Under fixed uniform circular array (UCA), 3-D parameter estimation of a source whose half-wavelength is smaller than the array aperture would suffer from a serious phase ambiguity problem, which also appears in a recently proposed phase-based algorithm. In this paper, by using the
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Under fixed uniform circular array (UCA), 3-D parameter estimation of a source whose half-wavelength is smaller than the array aperture would suffer from a serious phase ambiguity problem, which also appears in a recently proposed phase-based algorithm. In this paper, by using the centro-symmetry of UCA with an even number of sensors, the source’s angles and range can be decoupled and a novel algorithm named subarray grouping and ambiguity searching (SGAS) is addressed to resolve angle ambiguity. In the SGAS algorithm, each subarray formed by two couples of centro-symmetry sensors can obtain a batch of results under different ambiguities, and by searching the nearest value among subarrays, which is always corresponding to correct ambiguity, rough angle estimation with no ambiguity is realized. Then, the unambiguous angles are employed to resolve phase ambiguity in a phase-based 3-D parameter estimation algorithm, and the source’s range, as well as more precise angles, can be achieved. Moreover, to improve the practical performance of SGAS, the optimal structure of subarrays and subarray selection criteria are further investigated. Simulation results demonstrate the satisfying performance of the proposed method in 3-D source localization. Full article
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Open AccessArticle A Type-2 Block-Component-Decomposition Based 2D AOA Estimation Algorithm for an Electromagnetic Vector Sensor Array
Sensors 2017, 17(5), 963; doi:10.3390/s17050963
Received: 18 January 2017 / Revised: 17 April 2017 / Accepted: 18 April 2017 / Published: 27 April 2017
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Abstract
This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of
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This paper investigates a two-dimensional angle of arrival (2D AOA) estimation algorithm for the electromagnetic vector sensor (EMVS) array based on Type-2 block component decomposition (BCD) tensor modeling. Such a tensor decomposition method can take full advantage of the multidimensional structural information of electromagnetic signals to accomplish blind estimation for array parameters with higher resolution. However, existing tensor decomposition methods encounter many restrictions in applications of the EMVS array, such as the strict requirement for uniqueness conditions of decomposition, the inability to handle partially-polarized signals, etc. To solve these problems, this paper investigates tensor modeling for partially-polarized signals of an L-shaped EMVS array. The 2D AOA estimation algorithm based on rank- ( L 1 , L 2 , · ) BCD is developed, and the uniqueness condition of decomposition is analyzed. By means of the estimated steering matrix, the proposed algorithm can automatically achieve angle pair-matching. Numerical experiments demonstrate that the present algorithm has the advantages of both accuracy and robustness of parameter estimation. Even under the conditions of lower SNR, small angular separation and limited snapshots, the proposed algorithm still possesses better performance than subspace methods and the canonical polyadic decomposition (CPD) method. Full article
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Open AccessArticle Direction of Arrival Estimation for MIMO Radar via Unitary Nuclear Norm Minimization
Sensors 2017, 17(4), 939; doi:10.3390/s17040939
Received: 25 February 2017 / Revised: 19 April 2017 / Accepted: 20 April 2017 / Published: 24 April 2017
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Abstract
In this paper, we consider the direction of arrival (DOA) estimation issue of noncircular (NC) source in multiple-input multiple-output (MIMO) radar and propose a novel unitary nuclear norm minimization (UNNM) algorithm. In the proposed method, the noncircular properties of signals are used to
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In this paper, we consider the direction of arrival (DOA) estimation issue of noncircular (NC) source in multiple-input multiple-output (MIMO) radar and propose a novel unitary nuclear norm minimization (UNNM) algorithm. In the proposed method, the noncircular properties of signals are used to double the virtual array aperture, and the real-valued data are obtained by utilizing unitary transformation. Then a real-valued block sparse model is established based on a novel over-complete dictionary, and a UNNM algorithm is formulated for recovering the block-sparse matrix. In addition, the real-valued NC-MUSIC spectrum is used to design a weight matrix for reweighting the nuclear norm minimization to achieve the enhanced sparsity of solutions. Finally, the DOA is estimated by searching the non-zero blocks of the recovered matrix. Because of using the noncircular properties of signals to extend the virtual array aperture and an additional real structure to suppress the noise, the proposed method provides better performance compared with the conventional sparse recovery based algorithms. Furthermore, the proposed method can handle the case of underdetermined DOA estimation. Simulation results show the effectiveness and advantages of the proposed method. Full article
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Open AccessCommunication Fast Noncircular 2D-DOA Estimation for Rectangular Planar Array
Sensors 2017, 17(4), 840; doi:10.3390/s17040840
Received: 20 February 2017 / Revised: 28 March 2017 / Accepted: 31 March 2017 / Published: 12 April 2017
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Abstract
A novel scheme is proposed for direction finding with uniform rectangular planar array. First, the characteristics of noncircular signals and Euler’s formula are exploited to construct a new real-valued rectangular array data. Then, the rotational invariance relations for real-valued signal space are depicted
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A novel scheme is proposed for direction finding with uniform rectangular planar array. First, the characteristics of noncircular signals and Euler’s formula are exploited to construct a new real-valued rectangular array data. Then, the rotational invariance relations for real-valued signal space are depicted in a new way. Finally the real-valued propagator method is utilized to estimate the pairing two-dimensional direction of arrival (2D-DOA). The proposed algorithm provides better angle estimation performance and can discern more sources than the 2D propagator method. At the same time, it has very close angle estimation performance to the noncircular propagator method (NC-PM) with reduced computational complexity. Full article
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Open AccessArticle Underdetermined DOA Estimation of Quasi-Stationary Signals Using a Partly-Calibrated Array
Sensors 2017, 17(4), 702; doi:10.3390/s17040702
Received: 14 February 2017 / Revised: 23 March 2017 / Accepted: 26 March 2017 / Published: 28 March 2017
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Abstract
Quasi-stationary signals have been widely found in practical applications, which have time-varying second-order statistics while staying static within local time frames. In this paper, we develop a robust direction-of-arrival (DOA) estimation algorithm for quasi-stationary signals based on the Khatri–Rao (KR) subspace approach. A
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Quasi-stationary signals have been widely found in practical applications, which have time-varying second-order statistics while staying static within local time frames. In this paper, we develop a robust direction-of-arrival (DOA) estimation algorithm for quasi-stationary signals based on the Khatri–Rao (KR) subspace approach. A partly-calibrated array is considered, in which some of the sensors have an inaccurate knowledge of the gain and phase. In detail, we first develop a closed-form solution to estimate the unknown sensor gains and phases. The array is then calibrated using the estimated sensor gains and phases which enables the improved DOA estimation. To reduce the computational complexity, we also proposed a reduced-dimensional method for DOA estimation. The exploitation of the KR subspace approach enables the proposed method to achieve a larger number of degrees-of-freedom, i.e., more sources than sensors can be estimated. The unique identification condition for the proposed method is also derived. Simulation results demonstrate the effectiveness of the proposed underdetermined DOA estimation algorithm for quasi-stationary signals. Full article
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Open AccessArticle DOA Estimation of Coherent Signals on Coprime Arrays Exploiting Fourth-Order Cumulants
Sensors 2017, 17(4), 682; doi:10.3390/s17040682
Received: 15 February 2017 / Revised: 10 March 2017 / Accepted: 23 March 2017 / Published: 25 March 2017
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Abstract
This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime array. The
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This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime array. The special structure of the FCM is combined with the array configuration to resolve the coherent signals. Since each sparse array of a coprime array is uniform, a series of overlapping identical subarrays can be extracted. Using this property, we propose a generalized spatial smoothing scheme applied to the FCM. From the smoothed FCM, the DOAs of both the coherent and independent signals can be successfully estimated on the pseudo-spectrum generated by the fourth-order MUSIC algorithm. To overcome the problem of occasional false peaks appearing on the pseudo-spectrum, we use a supplementary sparse array whose inter-sensor spacing is coprime to that of either existing sparse array. From the combined spectrum aided by the supplementary sensors, the false peaks are removed while the true peaks remain. The effectiveness of the proposed methods is demonstrated by simulation examples. Full article
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Open AccessArticle DOA Estimation Based on Real-Valued Cross Correlation Matrix of Coprime Arrays
Sensors 2017, 17(3), 638; doi:10.3390/s17030638
Received: 14 January 2017 / Revised: 23 February 2017 / Accepted: 17 March 2017 / Published: 20 March 2017
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Abstract
A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the
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A fast direction of arrival (DOA) estimation method using a real-valued cross-correlation matrix (CCM) of coprime subarrays is proposed. Firstly, real-valued CCM with extended aperture is constructed to obtain the signal subspaces corresponding to the two subarrays. By analysing the relationship between the two subspaces, DOA estimations from the two subarrays are simultaneously obtained with automatic pairing. Finally, unique DOA is determined based on the common results from the two subarrays. Compared to partial spectral search (PSS) method and estimation of signal parameter via rotational invariance (ESPRIT) based method for coprime arrays, the proposed algorithm has lower complexity but achieves better DOA estimation performance and handles more sources. Simulation results verify the effectiveness of the approach. Full article
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