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Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security

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

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 60767

Special Issue Editors

Prof. Dr. Rongxing Lu

grade E-Mail Website
Guest Editor
Faculty of Computer Science, University of New Brunswick, Fredericton, NB E3A 0A1, Canada
Interests: blockchain; distributed concensus; distributed ledger; smart contract; security; privacy, trust, applications
Special Issues, Collections and Topics in MDPI journals
Dr. Chengwei Zhou

E-Mail Website
Guest Editor
College of Control Science and Engineering, Zhejiang University, Hangzhou, China
Interests: array signal processing; direction-of-arrival estimation, adaptive beamforming
Dr. Zhiguo Shi

E-Mail Website
Guest Editor
College of Information and Electronic Engineering, Zhejiang University, Hangzhou 310058, China
Interests: array signal processing; positioning and target tracking; mobile crowd-sensing; data acquisition in IoT; secure and privacy in IoT; signal processing for communications
Special Issues, Collections and Topics in MDPI journals
Dr. Yujie Gu

E-Mail
Guest Editor
Department of Electrical and Computer Engineering, Temple University, Philadelphia, USA
Interests: radar signal processing; statistical and array signal processing; wireless communications; compressive sensing and sparse reconstruction; optimization techniques
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past few years, there have been several exciting breakthroughs in the field of sensor array and multichannel signal processing, which are promising to expand its applications in radar, sonar, acoustics, speech, radio astronomy, wireless communications, etc. Specifically, the sparse arrays promote the performance of resolution, degrees-of-freedom, and efficiency for both direction-of-arrival (DOA) estimation and adaptive beamforming. The new insights into tensors and matrices create novel algebra techniques to pursuit super-resolution and its explicit performance guarantee. The emerging data-driven approaches on array signal processing forms a revolutionary upgradation from sensors to information. Nevertheless, there are still many fundamental theoretical and technical challenges for the practical sensor array applications, such as (a) the relations and substantial distinctions of the algorithm design on DOA estimation and adaptive beamforming under the sparse array signal processing circumstances, as well as the optimized array configuration and output performance; (b) the robust and efficient tensor-based and matrix-based signal processing techniques which will achieve better final performance; (c) the realistic implementation of machine learning to the fundamental problems in array signal processing, and the optimized training model design. Therefore, there is a pressing demand for developing innovative and efficacious array signal processing theories and techniques for future system implementation.

On the other hand, the Internet of Things (IoT), as an emerging technique, has brought us many opportunities today. However, more connected devices in IoT mean more attack vectors and more possibilities for adversaries to target us, and the IoT security challenges therefore cannot be ignored. Although many research efforts have been put into 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. Since array signal processing techniques owe their advantages to source detection, localization, and tracking, they can be utilized to provide intrusion detection and location-based IoT devices authentication. Therefore, the incorporation of sensor array and multichannel signal processing in IoT security contains huge research potential.

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

  • Adaptive beamforming;
  • Direction-of-Arrival estimation;
  • Space–time adaptive processing;
  • Tensor modeling and processing;
  • Sparse array, MIMO array, and massive array;
  • Compressive sensing and sparsity approaches;
  • Off-grid and gridless solutions to super-resolution;
  • Machine learning and data analytics for array signal processing;
  • Array applications to radar, acoustics, wireless communications, industrial IoT;
  • Array applications in security detection in IoT;
  • Array applications in location-based authentication in IoT.

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

Manuscript Submission Information

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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 and multichannel signal processing
  • Adaptive beamforming
  • Direction-of-arrival estimation
  • Sparse array signal processing
  • Tensor modeling and machine learning
  • Signal processing for IoT security

Published Papers (23 papers)

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12 pages, 466 KiB  
Communication
An Efficient Near-Field Localization Method of Coherently Distributed Strictly Non-circular Signals
by Meidong Kuang, Ling Wang, Yuexian Wang and Jian Xie
Sensors 2020, 20(18), 5176; https://doi.org/10.3390/s20185176 - 10 Sep 2020
Cited by 3 | Viewed by 1775
Abstract
For the near-field localization of non-circular distributed signals with spacial probability density functions (PDF), a novel algorithm is proposed in this paper. The traditional algorithms dealing with the distributed source are only for the far-field sources, and they need two-dimensional (2D) search or [...] Read more.
For the near-field localization of non-circular distributed signals with spacial probability density functions (PDF), a novel algorithm is proposed in this paper. The traditional algorithms dealing with the distributed source are only for the far-field sources, and they need two-dimensional (2D) search or omit the angular spread parameter. As a result, these algorithms are no longer inapplicable for near-filed localization. Hence the near-filed sources that obey a classical probability distribution are studied and the corresponding specific expressions are given, providing merits for the near-field signal localization. Additionally, non-circularity of the incident signal is taken into account in order to improve the estimation accuracy. For the steering vector of spatially distributed signals, we first give an approximate expression in a non-integral form, and it provides the possibility of separating the parameters to be estimated from the spatially discrete parameters of the signal. Next, based on the rank-reduced (RARE) algorithm, direction of arrival (DOA) and range can be obtained through two one-dimensional (1-D) searches separately, and thus the computational complexity of the proposed algorithm is reduced significantly, and improvements to estimation accuracy and identifiability are achieved, compared with other existing algorithms. Finally, the effectiveness of the algorithm is verified by simulation. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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17 pages, 3103 KiB  
Article
Parameter Estimation for Two-Dimensional Incoherently Distributed Source with Double Cross Arrays
by Tao Wu, Yiwen Li, Zhenghong Deng, Bo Feng and Xinping Ma
Sensors 2020, 20(16), 4562; https://doi.org/10.3390/s20164562 - 14 Aug 2020
Cited by 1 | Viewed by 1523
Abstract
A direction of arrival (DOA) estimator for two-dimensional (2D) incoherently distributed (ID) sources is presented under proposed double cross arrays, satisfying both the small interval of parallel linear arrays and the aperture equalization in the elevation and azimuth dimensions. First, by virtue of [...] Read more.
A direction of arrival (DOA) estimator for two-dimensional (2D) incoherently distributed (ID) sources is presented under proposed double cross arrays, satisfying both the small interval of parallel linear arrays and the aperture equalization in the elevation and azimuth dimensions. First, by virtue of a first-order Taylor expansion for array manifold vectors of parallel linear arrays, the received signal of arrays can be reconstructed by the products of generalized manifold matrices and extended signal vectors. Then, the rotating invariant relations concerning the nominal elevation and azimuth are derived. According to the rotating invariant relationships, the rotating operators are obtained through the subspace of the covariance matrix of the received vectors. Last, the angle matching approach and angular spreads are explored based on the Capon principle. The proposed method for estimating the DOA of 2D ID sources does not require a spectral search and prior knowledge of the angular power density function. The proposed DOA estimation has a significant advantage in terms of computational cost. Investigating the influence of experimental conditions and angular spreads on estimation, numerical simulations are carried out to validate the effectiveness of the proposed method. The experimental results show that the algorithm proposed in this paper has advantages in terms of estimation accuracy, with a similar number of sensors and the same experimental conditions when compared with existing methods, and that it shows a robustness in cases of model mismatch. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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19 pages, 6247 KiB  
Article
A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals
by Qing Ye, Shaohu Liu and Changhua Liu
Sensors 2020, 20(15), 4300; https://doi.org/10.3390/s20154300 - 01 Aug 2020
Cited by 11 | Viewed by 3048
Abstract
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of [...] Read more.
Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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16 pages, 5128 KiB  
Article
A Single-Dataset-Based Pre-Processing Joint Domain Localized Algorithm for Clutter-Suppression in Shipborne High-Frequency Surface-Wave Radar
by Liang Guo, Xin Zhang, Di Yao, Qiang Yang, Yang Bai and Weibo Deng
Sensors 2020, 20(13), 3773; https://doi.org/10.3390/s20133773 - 05 Jul 2020
Cited by 3 | Viewed by 2255
Abstract
Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression [...] Read more.
Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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15 pages, 1669 KiB  
Article
Robust Adaptive Beamforming with Optimal Covariance Matrix Estimation in the Presence of Gain-Phase Errors
by Di Yao, Xin Zhang, Bin Hu, Qiang Yang and Xiaochuan Wu
Sensors 2020, 20(10), 2930; https://doi.org/10.3390/s20102930 - 21 May 2020
Cited by 3 | Viewed by 2566
Abstract
An adaptive beamformer is sensitive to model mismatch, especially when the desired signal exists in the training samples. Focusing on the problem, this paper proposed a novel adaptive beamformer based on the interference-plus-noise covariance (INC) matrix reconstruction method, which is robust with gain-phase [...] Read more.
An adaptive beamformer is sensitive to model mismatch, especially when the desired signal exists in the training samples. Focusing on the problem, this paper proposed a novel adaptive beamformer based on the interference-plus-noise covariance (INC) matrix reconstruction method, which is robust with gain-phase errors for uniform or sparse linear array. In this beamformer, the INC matrix is reconstructed by the estimated steering vector (SV) and the corresponding individual powers of the interference signals, as well as noise power. Firstly, a gain-phase errors model of the sensors is deduced based on the first-order Taylor series expansion. Secondly, sensor gain-phase errors, the directions of the interferences, and the desired signal can be accurately estimated by using an alternating descent method. Thirdly, the interferences and noise powers are estimated by solving a quadratic optimization problem. To reduce the computational complexity, we derive the closed-form solutions of the second and third steps with compressive sensing and total least squares methods. Simulation results and measured data demonstrate that the performance of the proposed beamformer is always close to the optimum, and outperforms other tested methods in the case of gain-phase errors. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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15 pages, 3673 KiB  
Article
Array Diagnosis and DOA Estimation for Coprime Array under Sensor Failures
by Bing Sun, Chenxi Wu and Huailin Ruan
Sensors 2020, 20(9), 2735; https://doi.org/10.3390/s20092735 - 11 May 2020
Cited by 4 | Viewed by 2643
Abstract
A coprime array of N sensors can achieve O ( N 2 ) degrees of freedom (DOFs) by possessing a uniform linear array segment of size O ( N 2 ) in the difference coarray. However, the structure of difference coarray is sensitive [...] Read more.
A coprime array of N sensors can achieve O ( N 2 ) degrees of freedom (DOFs) by possessing a uniform linear array segment of size O ( N 2 ) in the difference coarray. However, the structure of difference coarray is sensitive to sensor failures. Once the sensor fails, the impact of failure sensors on the coarray structure may decrease the DOFs and cause direction finding failure. Therefore, the direction of arrival (DOA) estimation of coprime arrays with sensor failures is a significant but challenging topic for investigation. Driven by the need for remedial measures, an efficient detection strategy is developed to diagnose the coprime array. Furthermore, based on the difference coarray, we divide the sensor failures into two scenarios. For redundant sensor failure scenarios, the structure of difference coarray remains unchanged, and the coarray MUSIC (CO-MUSIC) algorithm is applied for DOA estimation. For non-redundant sensor failure scenarios, the consecutive lags of the difference coarray will contain holes, which hinder the application of CO-MUSIC. We employ Singular Value Thresholding (SVT) algorithm to fill the holes with covariance matrix reconstruction. Specifically, the covariance matrix is reconstructed into a matrix with zero elements, and the SVT algorithm is employed to perform matrix completion, thereby filling the holes. Finally, we employ root-MUSIC for DOA estimation. Simulation results verify the effectiveness of the proposed methods. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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19 pages, 875 KiB  
Article
Low Complexity Beamspace Super Resolution for DOA Estimation of Linear Array
by Jie Pan and Fu Jiang
Sensors 2020, 20(8), 2222; https://doi.org/10.3390/s20082222 - 15 Apr 2020
Cited by 8 | Viewed by 2505
Abstract
Beamspace processing has become much attractive in recent radar and wireless communication applications, since the advantages of complexity reduction and of performance improvements in array signal processing. In this paper, we concentrate on the beamspace DOA estimation of linear array via atomic norm [...] Read more.
Beamspace processing has become much attractive in recent radar and wireless communication applications, since the advantages of complexity reduction and of performance improvements in array signal processing. In this paper, we concentrate on the beamspace DOA estimation of linear array via atomic norm minimization (ANM). The existed generalized linear spectrum estimation based ANM approaches suffer from the high computational complexity for large scale array, since their complexity depends upon the number of sensors. To deal with this problem, we develop a low dimensional semidefinite programming (SDP) implementation of beamspace atomic norm minimization (BS-ANM) approach for DFT beamspace based on the super resolution theory on the semi-algebraic set. Then, a computational efficient iteration algorithm is proposed based on alternating direction method of multipliers (ADMM) approach. We develop the covariance based DOA estimation methods via BS-ANM and apply the BS-ANM based DOA estimation method to the channel estimation problem for massive MIMO systems. Simulation results demonstrate that the proposed methods exhibit the superior performance compared to the state-of-the-art counterparts. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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15 pages, 4088 KiB  
Article
2D-DOD and 2D-DOA Estimation for a Mixture of Circular and Strictly Noncircular Sources Based on L-Shaped MIMO Radar
by Jiaxiong Fang, Yonghong Liu, Yifang Jiang, Yang Lu, Zehao Zhang, Hua Chen and Laihua Wang
Sensors 2020, 20(8), 2177; https://doi.org/10.3390/s20082177 - 12 Apr 2020
Cited by 3 | Viewed by 2464
Abstract
In this paper, a joint diagonalization based two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) estimation method for a mixture of circular and strictly noncircular (NC) sources is proposed based on an L-shaped bistatic multiple input multiple output [...] Read more.
In this paper, a joint diagonalization based two dimensional (2D) direction of departure (DOD) and 2D direction of arrival (DOA) estimation method for a mixture of circular and strictly noncircular (NC) sources is proposed based on an L-shaped bistatic multiple input multiple output (MIMO) radar. By making full use of the L-shaped MIMO array structure to obtain an extended virtual array at the receive array, we first combine the received data vector and its conjugated counterpart to construct a new data vector, and then an estimating signal parameter via rotational invariance techniques (ESPRIT)-like method is adopted to estimate the DODs and DOAs by joint diagonalization of the NC-based direction matrices, which can automatically pair the four dimensional (4D) angle parameters and solve the angle ambiguity problem with common one-dimensional (1D) DODs and DOAs. In addition, the asymptotic performance of the proposed algorithm is analyzed and the closed-form stochastic Cramer–Rao bound (CRB) expression is derived. As demonstrated by simulation results, the proposed algorithm has outperformed the existing one, with a result close to the theoretical benchmark. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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26 pages, 5990 KiB  
Article
ECCM Schemes against Deception Jamming Using OFDM Radar with Low Global PAPR
by Xinhai Wang, Gong Zhang, Xiangmin Wang, Qingqing Song and Fangqing Wen
Sensors 2020, 20(7), 2071; https://doi.org/10.3390/s20072071 - 07 Apr 2020
Cited by 5 | Viewed by 3182
Abstract
In this paper, a type of effective electronic counter-countermeasures (ECCM) technique for suppressing the high-power deception jamming using an orthogonal frequency division multiplexing (OFDM) radar is proposed. Concerning the velocity deception jamming, the initial phases of the pulses transmitted in a coherent processing [...] Read more.
In this paper, a type of effective electronic counter-countermeasures (ECCM) technique for suppressing the high-power deception jamming using an orthogonal frequency division multiplexing (OFDM) radar is proposed. Concerning the velocity deception jamming, the initial phases of the pulses transmitted in a coherent processing interval (CPI) are designed to minimize the jamming power within a specific range, forming a notch around the jamming in the Doppler spectrum. For the purpose of suppressing the range deception jamming and the joint range-velocity deception jamming, the phase codes of the subcarriers belonging to the OFDM pulses are optimized to minimize the jamming power, distributing some specific bands in the range and the range-velocity domain, respectively. According to Parseval’s theorem, the phase encoding, acting as the coding manner of the OFDM subcarriers can ensure that the energy of each OFDM symbol stays the same. It is worth noticing that the phase codes of the OFDM subcarriers can influence the peak-to-average power ratio (PAPR). Thus, an optimization problem is formulated to optimize the phase codes of the subcarriers under the constraint of global PAPR, which can regulate the PAPRs of multiple OFDM symbols at the same time. The proposed problem is non-convex; therefore, it is a huge challenge to tackle. Then we present a method named by the phase-only alternating direction method multipliers (POADMM) to solve the aforementioned optimization problem. Some necessary simulation results are provided to demonstrate the effectiveness of the proposed radar signaling strategy Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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22 pages, 3057 KiB  
Article
Efficient Two-Dimensional Direction Finding Algorithm for Rectilinear Sources Under Unknown Mutual Coupling
by Jian Xie, Qiuping Wang, Yuexian Wang and Xin Yang
Sensors 2020, 20(7), 1914; https://doi.org/10.3390/s20071914 - 30 Mar 2020
Cited by 1 | Viewed by 2420
Abstract
Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such [...] Read more.
Digital communication signals in wireless systems may possess noncircularity, which can be used to enhance the degrees of freedom for direction-of-arrival (DOA) estimation in sensor array signal processing. On the other hand, the electromagnetic characteristics between sensors in uniform rectangular arrays (URAs), such as mutual coupling, may significantly deteriorate the estimation performance. To deal with this problem, a robust real-valued estimator for rectilinear sources was developed to alleviate unknown mutual coupling in URAs. An augmented covariance matrix was built up by extracting the real and imaginary parts of observations containing the circularity and noncircularity of signals. Then, the actual steering vector considering mutual coupling was reparameterized to make the rank reduction (RARE) property available. To reduce the computational complexity of two-dimensional (2D) spectral search, we individually estimated y-axis and x-axis direction-cosines in two stages following the principle of RARE. Finally, azimuth and elevation angle estimates were determined from the corresponding direction-cosines respectively. Compared with existing solutions, the proposed method is more computationally efficient, involving real-valued operations and decoupled 2D spectral searches into twice those of one-dimensional searches. Simulation results verified that the proposed method provides satisfactory estimation performance that is robust to unknown mutual coupling and close to the counterparts based on 2D spectral searches, but at the cost of much fewer calculations. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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15 pages, 1047 KiB  
Article
Robust Null Broadening Beamforming Based on Covariance Matrix Reconstruction via Virtual Interference Sources
by Jian Yang, Jian Lu, Xinxin Liu and Guisheng Liao
Sensors 2020, 20(7), 1865; https://doi.org/10.3390/s20071865 - 27 Mar 2020
Cited by 21 | Viewed by 3104
Abstract
When jammers move rapidly or an antenna platform travels at high speed, interference signals may move out of the null width in the array beampattern. Consequently, the interference suppression performance can be significantly degraded. To solve this problem, both the null broadening technique [...] Read more.
When jammers move rapidly or an antenna platform travels at high speed, interference signals may move out of the null width in the array beampattern. Consequently, the interference suppression performance can be significantly degraded. To solve this problem, both the null broadening technique and robust adaptive beamforming are considered in this paper. A novel null broadening beamforming method based on reconstruction of the interference-plus-noise covariance (INC) matrix is proposed, in order to broaden the null width and offset the motion of the interfering signals. In the moving case, a single interference signal can have multiple directions of arrival, which is equivalent to the existence of multiple interference sources. In the reconstruction of the INC matrix, several virtual interference sources are set up around each of the actual jammers, such that the nulls can be broadened. Based on the reconstructed INC and signal-plus-noise covariance (SNC) matrices, the steering vector of the desired signal can be obtained by solving a new convex optimization problem. Simulation results show that the proposed beamformer can effectively broaden the null width and deepen the null depth, and its performance in interference cancellation is robust against fast-moving jammers or array platform motion. Furthermore, the null depth can be controlled by adjusting the power parameters in the reconstruction process and, if the direction of interference motion is known, the virtual interference sources can be set to achieve better performance. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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17 pages, 3184 KiB  
Article
An Array Switching Strategy for Direction of Arrival Estimation with Coprime Linear Array in the Presence of Mutual Coupling
by Jinqing Shen, Yi He and Jianfeng Li
Sensors 2020, 20(6), 1629; https://doi.org/10.3390/s20061629 - 14 Mar 2020
Cited by 3 | Viewed by 2355
Abstract
While the coprime array still suffers from performance degradation due to the mutual coupling dominated by the interleaved subarrays, we propose an array switching strategy for coprime linear array (CLA) by utilizing the large inter-element spacings of the subarrays to mitigate the mutual [...] Read more.
While the coprime array still suffers from performance degradation due to the mutual coupling dominated by the interleaved subarrays, we propose an array switching strategy for coprime linear array (CLA) by utilizing the large inter-element spacings of the subarrays to mitigate the mutual coupling. Specifically, we first collect the signals by separately activating the two subarrays, where the severe mutual coupling effect is significantly reduced. As a result, well-performed initial direction of arrival (DOA) estimates can be achieved. Subsequently, we establish a quadratic optimization problem by reconstructing the contaminated steering vector of the total CLA elaborately to calculate the mutual coupling coefficients with the initial DOA estimates. Finally, we can obtain refined DOA estimates by an iteration procedure based on the estimated mutual coupling matrix. In addition, numerical simulations are provided to demonstrate the merits of the proposed scheme. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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13 pages, 326 KiB  
Communication
DOA Estimation Using Fourth-Order Cumulants in Nested Arrays with Structured Imperfections
by Baoping Wang and Junhao Zheng
Sensors 2020, 20(4), 994; https://doi.org/10.3390/s20040994 - 12 Feb 2020
Cited by 1 | Viewed by 2016
Abstract
Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is [...] Read more.
Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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19 pages, 409 KiB  
Article
Cumulant-Based DOA Estimation of Noncircular Signals against Unknown Mutual Coupling
by Baoping Wang and Junhao Zheng
Sensors 2020, 20(3), 878; https://doi.org/10.3390/s20030878 - 06 Feb 2020
Cited by 2 | Viewed by 2231
Abstract
To effectively find the direction of non-circular signals received by a uniform linear array (ULA) in the presence of non-negligible perturbations between array elements, i.e., mutual coupling, in colored noise, a direction of arrival (DOA) estimation approach in the context of high order [...] Read more.
To effectively find the direction of non-circular signals received by a uniform linear array (ULA) in the presence of non-negligible perturbations between array elements, i.e., mutual coupling, in colored noise, a direction of arrival (DOA) estimation approach in the context of high order statistics is proposed in this correspondence. Exploiting the non-circularity hidden behind a certain class of wireless communication signals to build up an augmented cumulant matrix, and carrying out a reformulation of the distorted steering vector to extract the angular information from the unknown mutual coupling, by exploiting the characteristic of mutual coupling, i.e., a limited operating range and an inverse relation of coupling effects to interspace, we develop a MUSIC-like estimator based on the rank-reduction (RARE) technique to directly determine directions of incident signals without mutual coupling compensation. Besides, we provide a solution to the problem of coherency between signals and mutual coupling between sensors co-existing, by selecting a middle sub-array to mitigate the undesirable effects and exploiting the rotation-invariant property to blindly separate the coherent signals into different groups to enhance the degrees of freedom. Compared with the existing robust DOA methods to the unknown mutual coupling under the framework of fourth-order cumulants (FOC), our work takes advantage of the larger virtual array and is able to resolve more signals due to greater degrees of freedom. Additionally, as the effective aperture is virtually extended, the developed estimator can achieve better performance under scenarios with high degree of mutual coupling between two sensors. Simulation results demonstrate the validity and efficiency of the proposed method. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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17 pages, 2367 KiB  
Article
A Novel Unitary ESPRIT Algorithm for Monostatic FDA-MIMO Radar
by Feilong Liu, Xianpeng Wang, Mengxing Huang, Liangtian Wan, Huafei Wang and Bin Zhang
Sensors 2020, 20(3), 827; https://doi.org/10.3390/s20030827 - 04 Feb 2020
Cited by 28 | Viewed by 3357
Abstract
A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property [...] Read more.
A novel unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm, for the joint direction of arrival (DOA) and range estimation in a monostatic multiple-input multiple-output (MIMO) radar with a frequency diverse array (FDA), is proposed. Firstly, by utilizing the property of Centro-Hermitian of the received data, the extended real-valued data is constructed to improve estimation accuracy and reduce computational complexity via unitary transformation. Then, to avoid the coupling between the angle and range in the transmitting array steering vector, the DOA is estimated by using the rotation invariance of the receiving subarrays. Thereafter, an automatic pairing method is applied to estimate the range of the target. Since phase ambiguity is caused by the phase periodicity of the transmitting array steering vector, a removal method of phase ambiguity is proposed. Finally, the expression of Cramér–Rao Bound (CRB) is derived and the computational complexity of the proposed algorithm is compared with the ESPRIT algorithm. The effectiveness of the proposed algorithm is verified by simulation results. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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19 pages, 976 KiB  
Article
Robust Sparse Bayesian Learning-Based Off-Grid DOA Estimation Method for Vehicle Localization
by Yun Ling, Huotao Gao, Sang Zhou, Lijuan Yang and Fangyu Ren
Sensors 2020, 20(1), 302; https://doi.org/10.3390/s20010302 - 05 Jan 2020
Cited by 10 | Viewed by 3508
Abstract
With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency [...] Read more.
With the rapid development of the Internet of Things (IoT), autonomous vehicles have been receiving more and more attention because they own many advantages compared with traditional vehicles. A robust and accurate vehicle localization system is critical to the safety and the efficiency of autonomous vehicles. The global positioning system (GPS) has been widely applied to the vehicle localization systems. However, the accuracy and the reliability of GPS have suffered in some scenarios. In this paper, we present a robust and accurate vehicle localization system consisting of a bistatic passive radar, in which the performance of localization is solely dependent on the accuracy of the proposed off-grid direction of arrival (DOA) estimation algorithm. Under the framework of sparse Bayesian learning (SBL), the source powers and the noise variance are estimated by a fast evidence maximization method, and the off-grid gap is effectively handled by an advanced grid refining strategy. Simulation results show that the proposed method exhibits better performance than the existing sparse signal representation-based algorithms, and performs well in the vehicle localization system. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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12 pages, 327 KiB  
Article
SPICE-ML Algorithm for Direction-of-Arrival Estimation
by Yu Zheng, Lutao Liu and Xudong Yang
Sensors 2020, 20(1), 119; https://doi.org/10.3390/s20010119 - 24 Dec 2019
Cited by 7 | Viewed by 2810
Abstract
Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal. However, the signal power estimated by sparse iterative covariance-based estimation approach is inaccurate, and the estimation performance is limited to [...] Read more.
Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal. However, the signal power estimated by sparse iterative covariance-based estimation approach is inaccurate, and the estimation performance is limited to direction grid. To solve the problem above, an algorithm combing the sparse iterative covariance-based estimation approach and maximum likelihood estimation is proposed. The signal power estimated by sparse iterative covariance-based estimation approach is corrected by a new iterative process based on the asymptotically minimum variance criterion. In addition, a refinement procedure is derived by minimizing a maximum likelihood function to overcome the estimation accuracy limitation imposed by direction grid. Simulation results verify the effectiveness of the proposed algorithm. Compared with sparse iterative covariance-based estimation approach, the proposed algorithm can achieve more accurate signal power and improved estimation performance. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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14 pages, 4353 KiB  
Article
Joint Angle and Frequency Estimation Using One-Bit Measurements
by Zeyang Li, Junpeng Shi, Xinhai Wang and Fangqing Wen
Sensors 2019, 19(24), 5422; https://doi.org/10.3390/s19245422 - 09 Dec 2019
Cited by 9 | Viewed by 2842
Abstract
Joint angle and frequency estimation is an important branch in array signal processing with numerous applications in radar, sonar, wireless communications, etc. Extensive attention has been paid and numerous algorithms have been developed. However, existing algorithms rely on accurately quantified measurements. In this [...] Read more.
Joint angle and frequency estimation is an important branch in array signal processing with numerous applications in radar, sonar, wireless communications, etc. Extensive attention has been paid and numerous algorithms have been developed. However, existing algorithms rely on accurately quantified measurements. In this paper, we stress the problem of angle and frequency estimation for sensor arrays using one-bit measurements. The relationship between the covariance matrices of one-bit measurement and that of the accurately quantified measurement is extended to the tensor domain. Moreover, a one-bit parallel factor analysis (PARAFAC) estimator is proposed. The simulation results show that the angle and frequency estimation can be quickly achieved and correctly paired. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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26 pages, 1277 KiB  
Article
Directional Modulation Technique Using a Polarization Sensitive Array for Physical Layer Security Enhancement
by Wei Zhang, Bin Li, Mingnan Le, Jun Wang and Jinye Peng
Sensors 2019, 19(24), 5396; https://doi.org/10.3390/s19245396 - 06 Dec 2019
Cited by 1 | Viewed by 2775
Abstract
Directional modulation (DM), as an emerging promising physical layer security (PLS) technique at the transmitter side with the help of an antenna array, has developed rapidly over decades. In this study, a DM technique using a polarization sensitive array (PSA) to produce the [...] Read more.
Directional modulation (DM), as an emerging promising physical layer security (PLS) technique at the transmitter side with the help of an antenna array, has developed rapidly over decades. In this study, a DM technique using a polarization sensitive array (PSA) to produce the modulation with different polarization states (PSs) at different directions is investigated. A PSA, as a vector sensor, can be employed for more effective DM for an additional degree of freedom (DOF) provided in the polarization domain. The polarization information can be exploited to transmit different data streams simultaneously at the same directions, same frequency, but with different PSs in the desired directions to increase the channel capacity, and with random PSs off the desired directions to enhance PLS. The proposed method has the capability of concurrently projecting independent signals into different specified spatial directions while simultaneously distorting signal constellation in all other directions. The symbol error rate (SER), secrecy rate, and the robustness of the proposed DM scheme are analyzed. Design examples for single- and multi-beam DM systems are also presented. Simulations corroborate that (1) the proposed method is more effective for PLS; (2) the proposed DM scheme is more power-efficient than the traditional artificial noise aided DM schemes; and (3) the channel capacity is significantly improved compared with conventional scalar antenna arrays. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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24 pages, 531 KiB  
Article
Directional Modulation-Enhanced Multiple Antenna Arrays for Secure and Precise Wireless Transmission
by Wei Zhang, Mingnan Le, Bin Li, Jun Wang and Jinye Peng
Sensors 2019, 19(22), 4833; https://doi.org/10.3390/s19224833 - 06 Nov 2019
Cited by 7 | Viewed by 2549
Abstract
Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. [...] Read more.
Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. However, it is possible that Eves are in the same direction as LUs in practical applications. As a result, signals received by Eves will be approximately the same or even in better quality than those received by LUs. To address the neighbor security issue, we introduce a multiple antenna arrays model at the transmitter side with the help of the artificial noise (AN)-aided DM technique to achieve secure and precise DM transmission in this paper. Meanwhile, to recover the mixed useful signals, two novel DM schemes based on single- and multi-carrier multiple antenna arrays model are proposed, respectively. In addition, the symbol error rate (SER), secrecy rate, and robustness performance of the proposed DM schemes were analyzed and simulated. Simulations validate the effectiveness of the proposed DM schemes and demonstrate that multiple antenna arrays model based DM methods outperform single antenna array model aided DM methods in security. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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14 pages, 410 KiB  
Article
A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements
by Tao Chen, Jian Yang and Muran Guo
Sensors 2019, 19(21), 4706; https://doi.org/10.3390/s19214706 - 29 Oct 2019
Cited by 11 | Viewed by 3046
Abstract
In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the [...] Read more.
In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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12 pages, 2428 KiB  
Letter
Gain-Phase Errors Calibration for a Linear Array Based on Blind Signal Separation
by Zheng Dai, Weimin Su and Hong Gu
Sensors 2020, 20(15), 4233; https://doi.org/10.3390/s20154233 - 29 Jul 2020
Cited by 5 | Viewed by 2162
Abstract
In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array [...] Read more.
In this paper, a non-iterative blind calibration algorithm for gain-phase errors is proposed. A mixing matrix is first obtained from the received observation data through blind signal separation. The mixing matrix is the product of the gain-phase error matrix and the ideal array manifold matrix. Then, a spatial spectrum is constructed by using the estimated mixed matrix. The direction corresponding to the maximum point of the spectral function is proved to be the azimuth of a certain source. Therefore, after the direction-of-arrival (DOA) is obtained by a one-dimensional spectrum search, the active calibration method can be used to estimate the gain-phase errors. The proposed algorithm is not limited to the calibration for uniform linear array (ULA), but also applicable to a non-uniform linear array. Moreover, the estimation performance of the algorithm will not be affected by the magnitude of the gain errors. Some simulations are given to verify the effectiveness and performance of the algorithm. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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14 pages, 313 KiB  
Letter
Doubly Covariance Matrix Reconstruction Based Blind Beamforming for Coherent Signals
by Zhuang Xie, Chongyi Fan, Jiahua Zhu and Xiaotao Huang
Sensors 2020, 20(12), 3595; https://doi.org/10.3390/s20123595 - 25 Jun 2020
Cited by 1 | Viewed by 2345
Abstract
This paper proposes a beamforming method in the presence of coherent multipath arrivals at the array. The proposed method avoids the prior knowledge or estimation of the directions of arrival (DOAs) of the direct path signal and the multipath signals. The interferences are [...] Read more.
This paper proposes a beamforming method in the presence of coherent multipath arrivals at the array. The proposed method avoids the prior knowledge or estimation of the directions of arrival (DOAs) of the direct path signal and the multipath signals. The interferences are divided into two groups based on their powers and the interference-plus-noise covariance matrix (INCM) is reconstructed through the doubly covariance matrix reconstruction concept. The composite steering vector (CSV) that accounts for the direct path signal and multipath signals is estimated as the principal eigenvector of the sample covariance matrix with interferences and noise removed. The optimal weight vector is finally computed using the INCM and the CSV. The proposed method involves no spatial smoothing and avoids reduction in the degree of freedom. Simulation results demonstrate the improved performance of the proposed method. Full article
(This article belongs to the Special Issue Recent Advances in Sensor Array and Multichannel Signal Processing with Its Applications to IoT Security)
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