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In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Coprime arrays (CPA) are extensively applied to estimate the direction of arrival (DOA). Yet, the effectiveness of existing DOA estimation algorithms for coprime arrays is predicated on the precise calibration of the array. Gain-phase errors in the sensors, when present, are magnified by the co-array, greatly impacting the performance of DOA estimation algorithms. To address the issue, this paper proposes a joint DOA estimation algorithm combining auxiliary arrays with atomic norm minimization. Exploiting the sub-array decomposition feature of coprime array, gain-phase error estimation is accomplished with the addition of precisely calibrated auxiliary arrays. Besides, the estimated gain-phase errors are used to correct the covariance of the signals received by the array. By combining array interpolation, all virtual sensors are fully utilized, and the interpolated virtual received signals are reconstructed into a Toeplitz matrix. The covariance matrix of these signals is then refined using atomic norm minimization, preparing the data for the final DOA estimation performed by subspace algorithms. In addition, for comparative analysis, the Cramér–Rao bound has also been derived under the condition of gain-phase errors, providing a benchmark for the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In the field of coherent direction of arrival (DOA) estimation, traditional subspace-based algorithms encounter difficulties due to the loss of rank in the signal covariance matrix. To mitigate this concern, we introduce a novel technique called augmented space smoothing (ASS). The proposed method exploits the maximum eigenvectors and their corresponding eigenvalues of the signal subspace, which benefits the utilize both auto-correlation and inter-correlation information. By assigning distinct weights to the auto-correlation and cross-correlation information, our method enables accurate direction-finding estimation of two fully coherent signals located at varying angular intervals. Furthermore, we provide a rigorous proof that the proposed ASS matrix efficiently recovers the matrix rank matching the source quantity. By utilizing these fundamental properties, our approach demonstrates a de-coherence ability to address the rank deficiency issue in traditional subspace-based algorithms used for coherent signal processing tasks. Compared to existing spatial smoothing methods, such as spatial smoothing pre-processing (SSP), modified spatial smoothing pre-processing (MSSP), subarrays cross-correlation (SCC), improved spatial smoothing (ISS), enhanced spatial smoothing (ESS) and enhanced spatial smoothing pre-processing based on signal space (ESS-SS), our proposed algorithm demonstrates a superior estimation performance. Finally, we have verified the effectiveness of our algorithm through the simulation results of signal-to-noise ratio (SNR), the number of snapshots, and angular separation. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets. [ABSTRACT FROM AUTHOR]

This article introduces an innovative method to improve the accuracy of estimating the Direction of Arrival (DOA) by utilizing non-uniform linear arrays (NULAs) operating at harmonic frequencies. Common DOA techniques face challenges in maintaining sufficient degrees of freedom (DOFs) while minimizing the number of sensors. Our approach takes advantage of the extended array aperture of NULAs and the diverse frequencies involved in harmonics to address these challenges. We explore various NULA structures, such as nested arrays, coprime arrays, and coprime arrays with compressed inter-element spacing (CACIS), each with unique characteristics and requirements for enhancing DOFs at the third and fifth harmonics. Simulation results demonstrate a substantial increase in DOFs, emphasizing the effectiveness and adaptability of our method in improving DOA estimation across different NULA configurations. This research contributes not only to theoretical advancements in antenna array processing but also offers practical insights, showcasing the potential of our method to significantly enhance DOA estimation accuracy in complex signal environments.

Most of the existing direction of arrival (DOA) estimation methods in impulsive noise scenario are based on the fractional low-order moment statistics (FLOSs), such as the robust covariation-based (ROC), fractional low-order moment (FLOM), and phased fractional low-order moment (PFLOM). However, an unknown order moment parameter $p$ needs to be selected in these approaches, which inevitably increases the computational load if the optimal value of the parameter $p$ is determined by a large number of Monte Carlo experiments. To address this issue, we propose the adaptive order moment function (AOMF) and improved AOMF (IAOMF), which are applicable to the existing FLOSs-based methods and can also be extended to the case of sparse arrays. Moreover, we analyze the performance of AOMF and IAOMF, and simulation experiments verify the effectiveness of proposed methods.

In this paper, the direction of arrival (DOA) estimation problem for the unfolded coprime linear array (UCLA) is researched. Existing common stacking subarray-based methods for the coprime array are invalid in the case of its subarrays, which have the same steering vectors of source angles. To solve the phase ambiguity problem, we reconstruct an improved unfolded coprime linear array (IUCLA) by rearranging the reference element of the prototype UCLA. Specifically, we design the multiple coprime inter pairs by introducing the third coprime integer, which can be pairwise with the other two integers. Then, the phase ambiguity problem can be solved via the multiple coprime property. Furthermore, we employ a spectral peak searching method that can exploit the whole aperture and full DOFs of the IUCLA to detect targets and achieve angle estimation. Meanwhile, the proposed method avoids extra processing in eliminating ambiguous angles, and reduces the computational complexity. Finally, the Cramer–Rao bound (CRB) and numerical simulations are provided to demonstrate the effectiveness and superiority of the proposed method.

Artificial neural networks‐based module for fast direction‐of‐arrival (DoA) estimation of the signal received by multi‐element textile wearable antenna array (TWAA) is proposed in this paper. The developed multilayer perceptron (MLP) DoA module considers the effects of changing the gain of the antenna elements, the distance between the antenna elements and their resonant frequencies during the movement of the TWAA wearer and the crumpling of the textile. The inputs of the MLP DoA module are the elements of the spatial correlation matrix of the signal sampled by the TWAA and the output is the angular position of the signal source in the azimuthal plane. The influence of the number of TWAA elements on the accuracy of the MLP DoA module in DoA estimation under textile crumple conditions is investigated. The performances of the MLP DoA module under increased noise conditions are investigated, as well as its behavior under different degrees of textile crumpling. A comparison was made between the proposed module and the corresponding root MUSIC DoA module in terms of accuracy and speed of DoA estimation. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), wherein the performance of traditional subspace-based DOA estimation algorithms deteriorates significantly. In this paper, we propose a correlation entropy-based infinite norm preprocessing algorithm, which can be applicable to any type of impulsive noise. Each snapshot of the sensor array data is processed by an exponential kernel function with the infinite norm, which can effectively combat the outliers. Furthermore, we construct the equivalent second-order covariance matrix and perform DOA estimation using classical subspace methods. Simulation results demonstrate the effectiveness of the proposed method for both symmetric α -stable distribution and the Gaussian mixture model. [ABSTRACT FROM AUTHOR]

Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of their improved estimation accuracy and reduced computational cost. However, few have considered the existence of a nested array (NA) with off-grid DOA estimation. In this study, we present a deep sparse Bayesian learning (DSBL) network to solve this problem. We first establish the signal model for off-grid DOA with NA. Then, we transform the array output into a real domain for neural networks. Finally, we construct and train the DSBL network to determine the on-grid spatial spectrum and off-grid value, where the loss function is calculated using reconstruction error and the sparsity of network output, and the layers correspond to the steps of the sparse Bayesian learning algorithm. We demonstrate that the DSBL network can achieve better generalization ability without training labels and large-scale training data. The simulation results validate the effectiveness of the DSBL network when compared with those of existing methods. [ABSTRACT FROM AUTHOR]

This paper investigates the direction of arrival (DOA) estimation of coherent signals with a moving coprime array (MCA). Spatial smoothing techniques are often used to deal with the covariance matrix of coherent signals, but they cannot be used in sparse arrays. Therefore, super-resolution algorithms such as multiple signal classification (MUSIC) cannot be applied in the DOA estimation of coherent signals in sparse arrays. In this study, we propose an enhanced spatial smoothing method specifically designed for MCA. Firstly, we combine the signals received by the MCA at different times, which can be regarded as a sparse array with a larger number of array sensors. Secondly, we describe how to compute the covariance matrix, derive the signal subspace by eigenvalue decomposition, and prove that the signal subspace is also equivalent to a received signal. Thirdly, we apply enhanced spatial smoothing to the signal subspace and construct a rank recovered covariance matrix. Finally, the DOA of coherent signals are well estimated by the MUSIC algorithm. The simulation results validate the improved performance of the proposed algorithm compared with traditional methods, particularly in scenarios with low signal-to-noise ratios. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Direction of arrival (DOA) estimation of mixed near‐field (NF) and far‐field (FF) signals is one of the key issues in the study of array signal processing. For more precise localisation performance, sparse arrays have drawn the attention of an increasing number of academics. Excitingly, sparse arrays enable larger array apertures and higher degrees of freedom (DOFs) than traditionally used uniform linear arrays. Consequently, this study proposes a transformed symmetric nested array (TSNA) for the passive localisation application regarding mixed NF and FF. There are three processes that go into building the proposed array. In the first step, we swap the positions of the uniform linear arrays and the sparse linear arrays in the nested array. In the second step, we place the corresponding array sensors extracted from the uniform linear array behind the uniform linear array to form a new subarray. In the last step, the TSNA is constructed by folding the formed array in half. Furthermore, we give the formula of the largest consecutive lags of the TSNA and its corresponding constructor. Finally, through theoretical analysis and computer performance simulation, this study illustrates the proposed array's numerous advantages over currently used symmetric sparse arrays.

In the field of direction of arrival (DOA) estimation for coherent sources, subspace-based model-driven methods exhibit increased computational complexity due to the requirement for eigenvalue decomposition. In this paper, we propose a new neural network, i.e., the signal space deep convolution (SSDC) network, which employs the signal space covariance matrix as the input and performs independent two-dimensional convolution operations on the symmetric real and imaginary parts of the input signal space covariance matrix. The proposed SSDC network is designed to address the challenging task of DOA estimation for coherent sources. Furthermore, we leverage the spatial sparsity of the output from the proposed SSDC network to conduct a spectral peak search for obtaining the associated DOAs. Simulations demonstrate that, compared to existing state-of-the-art deep learning-based DOA estimation methods for coherent sources, the proposed SSDC network achieves excellent results in both matching and mismatching scenarios between the training and test sets.

The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4 Q 1 Q 2 + 8 Q 1 − 5 , when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation. [ABSTRACT FROM AUTHOR]

Estimating the direction of arrival (DOA) of a single source signal from a single observation of an array data still plays an important part in practical application scenarios. To address the problem, this letter proposes two iterative, fast, and accurate approaches, namely, the Q-shift-based DOA estimation algorithm (QS-DOAEA) and the tradeoff between A&M and Q-shift-based DOA estimation algorithm (TAQ-DOAEA). QS-DOAEA adopts the interpolation of shifted discrete Fourier transform (DFT)coefficients to iteratively obtain the near-optimal estimation. TAQ-DOAEA employs the error function by simultaneously mixing the $q$ -shifted and half-shifted DFT coefficients, which only requires two iterations. Numerical results reveal that QS-DOAEA achieves significant improvement in terms of estimation accuracy and TAQ-DOAEA expedites the convergence. The proposed estimation algorithms demonstrate that they have an asymptotic variance that is at least 1.0013 times the asymptotic Cramér–Rao bound (ACRB), and provide more than $80\times $ reduction in the computational cost, compared to the baseline method. All our proposed algorithms and code are available at https://github.com/jn-z/. [ABSTRACT FROM AUTHOR]

This paper experimentally investigates a direction of arrival (DOA)-based wave source location estimation method for spectrum sharing in millimeter-wave band local and private fifth-generation (5G) mobile communication systems. In the DOA-based wave source location estimation, it is important to extract a line-of-sight (LOS) component from the received signals. Considering that in 5G systems, synchronization signal / physical broadcast channel (SS/PBCH) blocks are transmitted with beam-sweeping for initial access, our measurement system performs the DOA estimation based on the maximum-power block where the LOS component seems to be dominant. In the DOA estimation, taking advantage of the sparsity of the propagation channels in both angular space and delay time domains, our measurement system adopts the compressed sensing (CS)-based approach. As for the measurement antenna, a directional pattern is employed, which enables the estimation with only four antenna elements. We experimentally demonstrate the performance of the wave source location estimation in an outdoor environment using a commercial 28 GHz-band 5G base station (BS). Experimental results show that our measurement system achieves a location estimation error of 5.8 m based on 20 measurements.

Mutual coupling effect and degrees of freedom (DOFs) play a crucial role in the sparse array design. In this paper, we judiciously configure the dense subarray and sparse subarray of the nested coprime array with displaced subarrays (NCADiS) to obtain an improved NCADiS (INCADiS). Then, the dense subarray of the INCADiS is split into two parts and rearranged on the left and right sides of the sparse subarray of the INCADiS to structure an augmented nested coprime array with displaced subarrays (ANCADiS). We also derive the closed-form expressions for the element locations and the number of uniform DOFs (uDOFs) of the ANCADiS configurations with any number of elements. The proposed array configuration has the same uDOFs as the INCADiS and the prototype optimised NCADiS (PNCADiS), and at the same time achieves reduced mutual coupling. Some theoretical properties are proved, and numerical simulations are included to verify the superiority of the proposed configuration. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Direction of arrival (DOA) estimation of mixed near‐field (NF) and far‐field (FF) signals is one of the key issues in the study of array signal processing. For more precise localisation performance, sparse arrays have drawn the attention of an increasing number of academics. Excitingly, sparse arrays enable larger array apertures and higher degrees of freedom (DOFs) than traditionally used uniform linear arrays. Consequently, this study proposes a transformed symmetric nested array (TSNA) for the passive localisation application regarding mixed NF and FF. There are three processes that go into building the proposed array. In the first step, we swap the positions of the uniform linear arrays and the sparse linear arrays in the nested array. In the second step, we place the corresponding array sensors extracted from the uniform linear array behind the uniform linear array to form a new subarray. In the last step, the TSNA is constructed by folding the formed array in half. Furthermore, we give the formula of the largest consecutive lags of the TSNA and its corresponding constructor. Finally, through theoretical analysis and computer performance simulation, this study illustrates the proposed array's numerous advantages over currently used symmetric sparse arrays. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a new enhanced coprime array for direction of arrival (DOA) estimation. Coprime arrays are capable of estimating the DOA using coprime properties and outperforming uniform linear arrays. However, the associated algorithms are not directly applicable for estimating the DOA of coherent sources. To overcome this limitation, we propose an enhanced coprime array in this paper. By increasing the number of array sensors in the coprime array, it is feasible to enlarge the aperture of the array and these additional array sensors can be utilized to achieve spatial smoothing, thus enabling estimation of the DOA for coherent sources. Additionally, applying the spatial smoothing technique to the signal subspace, instead of the conventional spatial smoothing method, can further improve the ability to reduce noise interference and enhance the overall estimation result. Finally, DOA estimation is accomplished using the MUSIC algorithm. The simulation results demonstrate improved performance compared to traditional algorithms, confirming its feasibility.

Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of their improved estimation accuracy and reduced computational cost. However, few have considered the existence of a nested array (NA) with off-grid DOA estimation. In this study, we present a deep sparse Bayesian learning (DSBL) network to solve this problem. We first establish the signal model for off-grid DOA with NA. Then, we transform the array output into a real domain for neural networks. Finally, we construct and train the DSBL network to determine the on-grid spatial spectrum and off-grid value, where the loss function is calculated using reconstruction error and the sparsity of network output, and the layers correspond to the steps of the sparse Bayesian learning algorithm. We demonstrate that the DSBL network can achieve better generalization ability without training labels and large-scale training data. The simulation results validate the effectiveness of the DSBL network when compared with those of existing methods.

Direction of arrival (DOA) estimation with nested arrays has been widely investigated in the field of array signal processing, but most studies assume that the noise is Gaussian white noise. In practical situations, there may exist impulsive noise (a kind of heavy-tailed noise), wherein the performance of traditional subspace-based DOA estimation algorithms deteriorates significantly. In this paper, we propose a correlation entropy-based infinite norm preprocessing algorithm, which can be applicable to any type of impulsive noise. Each snapshot of the sensor array data is processed by an exponential kernel function with the infinite norm, which can effectively combat the outliers. Furthermore, we construct the equivalent second-order covariance matrix and perform DOA estimation using classical subspace methods. Simulation results demonstrate the effectiveness of the proposed method for both symmetric α-stable distribution and the Gaussian mixture model.

This paper proposes low-cost yet high-accuracy direction of arrival (DOA) estimation for the automotive frequency-modulated continuous-wave (FMCW) radar. The existing subspace-based DOA estimation algorithms suffer from either high-computational costs or low accuracy. We aim to solve such contradictory relation between complexity and accuracy by using randomized matrix approximation. Specifically, we apply an easily-interpretable randomized low-rank approximation to the covariance matrix (CM) and approximately compute its subspaces. That is, we first approximate CM R ∈ℂM×M through three sketch matrices, in the form of R≈QBQH, here the matrix Q∈ℂM×z contains the orthonormal basis for the range of the sketch matrix C∈ℂM×z which is extracted from R using randomized uniform column sampling and B∈ℂz×z is a weight-matrix reducing the approximation error. Relying on such approximation, we are able to accelerate the subspace computation by the orders of the magnitude without compromising estimation accuracy. Furthermore, we drive a theoretical error bound for the suggested scheme to ensure the accuracy of the approximation. As validated by the simulation results, the DOA estimation accuracy of the proposed algorithm, efficient multiple signal classification (E-MUSIC), is high, closely tracks standard MUSIC, and outperforms the well-known algorithms with tremendously reduced time complexity. Thus, the devised method can realize high-resolution real-time target detection in the emerging multiple input and multiple output (MIMO) automotive radar systems.

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this article, we propose a coprime array-based technique that enables global navigation satellite system (GNSS) receivers to reliably defend against spoofing attacks with a small time offset. The technique can not only detect spoofing, but also provide the direction of arrival (DOA) of the spoofing source. Our approach is implemented on the raw digital baseband signals, hence it does not need to perform the complex despreading and acquisition stage of the GNSS receiver. Crucially, the detector is still available when the number of signals (including spoofing and satellite signals) is more than the number of array elements. The simulation results demonstrate the effectiveness of the proposed technique in terms of DOA estimation and spoofing detection. [ABSTRACT FROM AUTHOR]

Improving safety is of general interest in aviation. Flight transponders used in collision avoidance systems advise pilots with information of potential collisions and avoidance resolutions. Some transponders are capable of transmitting the instantaneous position of the vehicle which might be turned off. To prevent autonomous flights from collisions, the transponder signals shall be used to estimate the direction-of-arrival (DoA). Recently, multimode antennas have been investigated for communication and localization purposes. Multimode antennas provide several orthogonal ports on a single radiator surface. This leads to a reduction of space and weight, which is of interest in airborne applications. To perform DoA estimation as well as communication, a suitable channel model is required. In this contribution, a channel model fulfilling the requirements for both multiple-input multiple-output (MIMO) communication and DoA estimation is proposed. This channel model relies on the manifold matrix representation of the UAV under consideration. The derived description of a manifold matrix for the UAV use-case is used for an optimization to achieve a close to omnidirectional radiation pattern. Therefore, different optimization functions are derived and their performance is compared. The results of the optimization using the calculated manifold matrix are verified by measurements taken in an antenna measurement chamber. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation based on Maximum Likelihood is a common method in array signal processing, with many practical applications, but the huge amount of calculation limits the practical application. To deal with such an Maximum Likelihood (ML) DOA estimation problem, firstly, the DOA estimation model with ML for acoustic vector sensor array is developed, where the optimization standard in various cases can be unified by converting the maximum of objective function to the minimum. Secondly, based on the Invasive Weed Optimization (IWO) method which is a novel biological evolutionary algorithm, a new Improved IWO (IIWO) algorithm for DOA estimation of the acoustic vector sensor array is proposed by using ML estimation. This algorithm simulates weed invasion process for DOA estimation by adjusting the non-linear harmonic exponent of IWO algorithm adaptively. The DOA estimation accuracy has been improved, and the computation of multidimensional nonlinear optimization for the ML method has been greatly reduced in the IIWO algorithm. Finally, compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE) method and Tuna Swarm Optimization(TSO) algorithm, numerical simulations show that the proposed algorithm has faster convergence rate, improved accuracy in terms of Root Mean Square Error (RMSE), lower computational complexity and more robust estimation performance for ML DOA estimation. The experiment with tracking the orientation of the motorboat by Microelectronic mechanical systems (MEMS) vector hydrophone array shows the superior performance of proposed IIWO algorithm in engineering application. Therefore, the proposed ML-DOA estimation with IIWO algorithm can take into account both resolution and computation. which can meet the requirements of real-time calculation and estimation accuracy in the actual environment.

This paper investigates the direction of arrival (DOA) estimation of coherent signals with a moving coprime array (MCA). Spatial smoothing techniques are often used to deal with the covariance matrix of coherent signals, but they cannot be used in sparse arrays. Therefore, super-resolution algorithms such as multiple signal classification (MUSIC) cannot be applied in the DOA estimation of coherent signals in sparse arrays. In this study, we propose an enhanced spatial smoothing method specifically designed for MCA. Firstly, we combine the signals received by the MCA at different times, which can be regarded as a sparse array with a larger number of array sensors. Secondly, we describe how to compute the covariance matrix, derive the signal subspace by eigenvalue decomposition, and prove that the signal subspace is also equivalent to a received signal. Thirdly, we apply enhanced spatial smoothing to the signal subspace and construct a rank recovered covariance matrix. Finally, the DOA of coherent signals are well estimated by the MUSIC algorithm. The simulation results validate the improved performance of the proposed algorithm compared with traditional methods, particularly in scenarios with low signal-to-noise ratios.

For the case where the antenna array has defects, most traditional direction of arrival (DOA) estimation methods are poorly adapted. In this paper, a DOA estimation method based on residual neural networks (ResNets) is introduced to obtain better adaptability to the defects of the antenna array and improve the generalization ability to unknown scenes. The framework of deep neural network mainly includes two parts: spatial classification networks (SCN) and ResNets. Firstly, the received signals are divided into corresponding signal space subregions, this operation can relieve the generalization burden of the ResNet that follows. Then, the output of the SCN is used as the input of several parallel ResNets, and each of ResNet judges whether there are signal components in the preset grid of the corresponding subspace regions. Finally, the results of all ResNets are combined into a spatial spectrum, and a spectrum peak search is performed to obtain the estimated direction of the signal. A large number of simulation results are available to confirm that the proposed method not only has excellent generalization ability, but also has high estimation accuracy under different array defects. [ABSTRACT FROM AUTHOR]

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds. [ABSTRACT FROM AUTHOR]

Although sparse spectrum fitting algorithm has good multi-target resolution, the performance is decreased under the influence of strong interference. In response to this problem, a sparse spectrum fitting algorithm using signal covariance matrix reconstruction and weighted sparse constraint is proposed. The algorithm uses iterative adaptive approach (IAA) to estimate spatial spectrum and divide signal region. The signal region is integrated to reconstruct the signal covariance matrix. On the other hand, the l 1 norm constraint is weighted in different angles by the inverse spatial spectrum estimated by IAA. Finally, the spatial spectrum of the signal region is fitted to detect and resolve the weak targets. Simulation and theoretical analysis show that the proposed algorithm has outform performance under the influence of strong interference. [ABSTRACT FROM AUTHOR]

Adaptive array calibration is a key technique, which significantly affects the performance of array signal processing in real environments. In this letter, we propose a general and simple method using real steering vectors (RSVs) for array calibration and high-precision direction of arrival estimation. The RSVs are constructed with the data sampled at intermediate frequency directly. All errors of the antennas and channels are taken into account. The effectiveness of this method is evaluated by simulation and measurement with a four elements microstrip linear array and a cylindrical conformal array. Both simulation and measurement results show that it is an efficient calibration method and is suitable for arrays with directional elements. [ABSTRACT FROM AUTHOR]

The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4Q1Q2+8Q1−5, when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation.

The use of the emerging hybrid analog-digital structure for future millimeter-wave communications has attracted much attention. Although this structure can reduce the power consumption considerably, the spatial covariance matrix (SCM), as the core of subspace-based direction of arrival (DOA) estimation algorithms, cannot be obtained directly. The beam sweeping algorithm (BSA) was proposed for SCM reconstruction from compressed measurements. However, the BSA is computationally intensive owing to the high-dimensional matrix-to-matrix multiplication and matrix inversion. To address this issue, a BSA algorithm with high accuracy and low computational-cost using the submatrix multiplication (BSASM) is proposed in this paper. By appropriately adjusting the weight, which consists of a switch and a phase shifter, connected to each antenna, the SCM can be accurately reconstructed with low-dimensional matrix-to-vector multiplication and 2-dimensional matrix inversion, thus reducing the computational-cost significantly. After SCM reconstruction, various DOA estimation algorithms can be exploited to obtain the DOA information. Simulation experiments are conducted to verify the performance of the proposed algorithm. The results indicate that using a substantially lower computational-cost, BSASM can reconstruct SCM more accurately than BSA. [ABSTRACT FROM AUTHOR]

The recent criteria of a preferable linear sparse array for a robust direction of arrival (DOA) estimation are: the closed-form expression for its sensor locations; the large central uniform linear array (ULA) segment in its resulting co-array; and the fewer sensor pairs with small separations in its arrangement. These features, in turn, will lead to a straightforward array design process, more sources being resolved and strong helpful performance for the array within a real-world mutual coupling environment. This paper introduces a new array called the dilated nested array (DNA), which takes into account all these considerations. The sensors of the DNA are arranged in a way that yields an effective difference co-array with a central virtual ULA part larger than that of the co-prime array and equal to that of the nested array, and in an equivalent importance, a physical arrangement with smaller numbers of sensor pairs with small separations than those of the super nested array, so that the array can notably reduce the mutual coupling effects in comparison. A displaced arrangement obtained from the DNA geometry is also introduced to further increase the co-array total number of DOFs and at the same time to decrease the numbers of sensor pairs with small separations. Extensive numerical simulations are included to demonstrate the effectiveness of the proposed DNAs over other sparse arrays. [ABSTRACT FROM AUTHOR]

Conventionalliterature on array signal processing (ASP) is based on the “capture first, process later” philosophy and to this end, signal processing algorithms are typically decoupled from the hardware. This poses fundamental limitations because if the sensors result in information loss, the algorithms may no longer be able to achieve their guaranteed performance. In this paper, our goal is to overcome the barrier of information loss via sensor saturation and clipping. This is a significant problem in application areas including physiological monitoring and extra-terrestrial exploration where the amplitudes may be unknown or larger than the dynamic range of the sensor. To overcome this fundamental bottleneck, we propose “computational arrays” which are based on a co-design approach so that a collaboration between the sensor array hardware and algorithms can be harnessed. Our work is inspired by the recently introduced unlimited sensing framework. In this context, our computational arrays encode the high-dynamic-range information by folding the signal amplitudes, thus introducing a new form of information loss in terms of the modulo measurements. On the decoding front, we develop mathematically guaranteed recovery algorithms for spatio-temporal array signal processing tasks that include DoA estimation, beamforming and signal reconstruction. Numerical examples corroborate the applicability of our approach and pave a path for the development of novel computational arrays for ASP. [ABSTRACT FROM AUTHOR]

The smart radio environment has become an eye-catching topic and is believed to own great potential in future wireless networks. Its essential demand lies in sensing and manipulating the electromagnetic (EM) fields accurately and simultaneously with the same aperture, which is hard to achieve using conventional methods due to the difficulty in integrating different systems. We propose a millimeter-wave space–time-coding digital metasurface (STCM) to realize simultaneous in situ direction finding and field manipulation. An analytic method of direction finding is first developed using the STCM modulated by a space–time-coding (STC) matrix while maintaining its original capability of EM field manipulations. In addition, three STC matrices corresponding to different EM functions are applied to the STCM to verify the feasibility and robustness of direction findings experimentally. Finally, a proof-of-concept prototype is built to exhibit different EM functions according to the incident angle detected simultaneously. The proposed concept sets the stage for the integrated implementation of the perception and response of the radio environment, which may find promising applications in future intelligent wireless networks and self-adaptive systems. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multi-polarization reception and occlusion effects of conformal arrays on complex carriers, this paper proposes an improved polarization multiple signal clasritication (MUSIC) algorithm based on the pattern matrix reconstruction steering vector. First, we model the conformal antenna array. Then, afier the arimuth and elevation component pattern data of each element are obtained, the pattern data is decomposed and reconstrucied to the steering vector matrix of the array. Finally, we estimaie the direction of arrival (DOA) and polarization parameters by the polarization MUSIC algorithm. Compared with the polarization MUSIC algorithm based on the theoretical sieering vector, the improved algorithm proposed in this paper has higher estimation accuracy while avoiding the ocdition effect. It also can reduce the amount of calculation. The condition is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

Sparse arrays have been an important concept in the study of direction of arrival (DOA) estimation since they can obtain the number of degrees of freedom (DOFs) is much larger than that of the number of physical sensors. However, the missing lags in the difference co-array reduce the DOF that is expected. Recently, array motion has received considerable attention, since this operation can fill the missing lags, increase the DOF, and enlarge the array aperture without adding extra physical sensors. In this study, we investigate the DOA estimation of array mounted on a moving platform and propose the condition and corresponding spatial sampling scheme that can result in a complete consecutive difference co-array. Specifically, we first explore the general expression of the synthetic array, which is generated by collecting the sampling data of a moving array at different times. After that, the closed-form expression for the difference co-array corresponding to the synthetic array is derived. Thirdly, we introduce the condition that must be satisfied to generate a hole-free difference co-array for movable arbitrary arrays. Then, based on this condition, we develop a non-uniform sampling method that can fill all the missing lags and lead to a fully consecutive difference co-array regardless of the physical geometry, which can therefore improve uniform DOFs (uDOFs). Finally, several classical arrays, including uniform linear array, coprime array, nested array, etc., are adopted to apply the proposed sampling scheme, and the closed-form expressions of the resulting uDOFs are analyzed in detail. Numerical simulations are presented to illustrate the effectiveness and superiority of the proposed sampling scheme in DOA estimation performance. [ABSTRACT FROM AUTHOR]