2D-DOA Estimation Research Articles

A compressed 2D-DOA and polarization estimation algorithm for mmWave polarized massive MIMO systems

Due to providing greater degrees-of-freedom (DOFs) and securer communication guarantees than traditional scalar array, polarized massive multiple-input multiple-output (MIMO) technique offers a prospective insight into millimeter-wave (mmWave) communication. In this paper, a compressed two-dimensional (2D) direction-of-arrival (DOA) and polarization estimation algorithm combining the compressive sampling propagator method with reduced-dimension multiple signal classification (CSPM-RDMUSIC) is developed for mmWave polarized massive MIMO systems. First, a synchronous compressive network is constructed to compress the high-dimensional output into a low-dimensional one. Thereafter, a propagator is structured to acquire the signal subspace. Subsequently, accurate 2D-DOA and polarization estimation are achieved using a coarse-refined strategy, where the vector cross-product technology is investigated for coarse 2D-DOA estimation and then RDMUSIC is exploited to perform 2D local search for accurate parameter estimation. Owing to integrating the CSPM framework with the RDMUSIC strategy, the developed approach provides high-precision parameter estimation with relative computational economy, supporting mmWave communication demands. What's more, it's suitable for arbitrary array geometry with high flexibility. Several experimental results validate the superiorities of the developed framework.

Geometric Algebra based 2D-DOA Estimation for Non-circular Signals with an Electromagnetic Vector Array

This paper presents two novel methods based on geometric algebra (GA) to estimate two-dimensional (2D) direction-of-arrival (DOA) of non-circular (NC) signals for uniform rectangular array (URA). Traditional methods treat the received NC signals as a long vector which will inevitably lose orthogonality inside each electromagnetic vector sensor (EMVS) and thus miss some information of second-order statistical properties. Furthermore, the computational complexity will also increase. By contrast, the GA-based estimating signal parameter via rotational invariance techniques (ESPRIT) and propagation method (PM) algorithms are proposed to estimation DOA of NC signals. Taking advantage of GA, the relationship among multidimensional signals can be maintained. First, the six components of the EMVS are represented as a multivector in GA space. Then, we construct the GA-based extended covariance matrix to utilize the signal information more completely. The DOA parameters can be estimated through the ESPRIT and PM principle. The proposed GA-based estimation of signal parameter via rotational invariance techniques for NC signals processing (GANC-ESPRIT) can estimate DOA with high estimation accuracy. The proposed GA-based propagation method for NC signals estimation (GANC-PM) uses linear transformation to calculate angle parameters. Our model has much lower memory requirements and less computational burden compared with long vector models. Simulation results demonstrate the robustness and superiority of the proposed GANC-ESPRIT algorithm in terms of angular resolution. Complexity analysis shows that the proposed GANC-PM algorithm performances better with less computations.

Deep learning based 2D-DOA estimation using L-shaped arrays

Direction-of-arrival (DOA) estimation problems arise in many applications such as wireless communication and localization. Recently, a number of deep learning (DL) based methods have been studied for one dimensional (1D) DOA estimation with relatively fewer studies for 2D-DOA estimation. In this study, we propose a low-complexity DL based method to estimate both elevation and azimuth DOAs of sources along with their pairing. To this end, first a classification neural network is proposed to estimate both elevation and azimuth DOAs of multiple sources. Next, a residual classification network is introduced to estimate the pairing between the estimated DOAs. In particular, we consider two perpendicular linear arrays which are located on z-axis and x-axis, respectively. The first proposed network uses the Sample Covariance Matrix (SCM) of the former array to estimate elevation angles and utilizes the latter array to estimate the corresponding azimuth DOAs. The second network, is responsible for estimating the pairing between the estimated elevation and azimuth DOA sets. Numerical simulations demonstrate the enhanced performance of our proposed 2D-DOA estimator scheme, surpassing existing 1D and 2D deep learning (DL) methods. Notably, our approach closely approaches the Ziv–Zakai bound (ZZB), particularly in low signal-to-noise ratio (SNR) and low-angle-difference scenarios, even in the presence of multiple highly correlated signals. Moreover, our complexity analysis validates the superiority of the proposed method.

Ambiguity-Free 2-D DOA and Polarization Estimation for Mirrored Linear Crossed-Dipole Arrays

Ambiguity-Free 2-D DOA and Polarization Estimation for Mirrored Linear Crossed-Dipole Arrays

Tensor-Based 2-D DOA Estimation for L-Shaped Nested Array

Tensor-Based 2-D DOA Estimation for L-Shaped Nested Array

Retracted: 2D-DOA Estimation for EMVS Array with Nonuniform Noise

Retracted: 2D-DOA Estimation for EMVS Array with Nonuniform Noise

A 2D-DOA Sparse Estimation Method with Total Variation Regularization for Spatially Extended Sources

In this paper, a novel two-dimensional direction of arrival (2D-DOA) estimation method with total variation regularization is proposed to deal with the problem of sparse DOA estimation for spatially extended sources. In a general sparse framework, the sparse 2D-DOA estimation problem is formulated with the regularization of extended source characteristics including spatial position grouping, acoustic signal block sparse, and correlation features. An extended sources acoustic model, two-dimensional array manifold and its complete representation, total variation regularization penalty term, and the regularization equation are built, and are utilized to seek the solutions where the non-zero coefficients are grouped together with optimum sparseness. A total variation sparse 2D-DOA estimation model is constructed by combining total variation regularization with LASSO. The model can be easily solved by the convex optimization algorithm, and the solving process can promote the sparsity of the solution on the spatial derivatives and the solution itself. The theoretical analysis results show that the steps of decorrelation processing and angle matching of traditional 2D-DOA estimation methods could be avoided when adopting the proposed method. The proposed method has better robustness to noise, better sparsity, and faster estimation speed with higher resolution than traditional methods. It is promising to provide a coherent sources sparse representation of a non-strictly sparse field.

2D-DOA Estimation in Multipath Using EMVS Rectangle Array

Electromagnetic vector sensor (EMVS) arrays bring an epochal opportunity for direction finding, as they enable the estimation of two-dimensional direction of arrival (2D-DOA) and polarization characteristics. In this paper, we revisit the 2D-DOA estimation problem in an EMVS rectangle array under multipath propagation. An improved subspace estimator is proposed, which addresses the rank-deficit problem through matrix arrangement, and the 2D-DOA and polarization parameters are estimated via combining the normalized vector cross-product with the least squares method. Our proposed method is suitable for a single snapshot scenario and offers superior accuracy compared to existing methods. To validate its effectiveness, several numerical simulations have been designed and conducted.

Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.

An Unambiguous 2D DOA Estimation Algorithm by a Large-Space L-Shaped Array

An Unambiguous 2D DOA Estimation Algorithm by a Large-Space L-Shaped Array

Compressive Sampling Framework for 2D-DOA and Polarization Estimation in mmWave Polarized Massive MIMO Systems

The polarized massive multiple-input multiple-output (MIMO) technique has been regarded as a promising solution to millimeter wave (mmWave) communication systems, because it experiences more degrees-of-freedom than the scalar configuration, and it represents a significant opportunity for secure communication. To deliver smart service to terminals, it is essential to provide base stations (BS) with the capability of terminal’s direction-of-arrival (DOA) awareness. In this paper, a compressive sampling (CS) framework is proposed for two-dimensional (2D) DOA and polarization estimation in mmWave polarized massive MIMO systems. The proposed approach first reduces the data volume via a reduced-dimension matrix. Then it computes the signal subspace via the eigendecomposition of the compressed array measurement. Thereafter, the rotational invariance characteristic is utilized to form a normalized polarization steering vector. Finally, 2D-DOA and polarization are estimated by incorporating the Poynting vector and the least squares (LS) techniques. The proposed architecture is computationally much more economical than existing algorithms. Besides, it allows a mmWave BS to provide comparable estimation performance with arbitrary sensor geometry, which is more flexible than most of the existing architectures. Furthermore, it is robust to the sensor position error. Numerical simulations verify the advantages of the proposed framework.

Sparse Billboard and T-Shaped Arrays for Two-Dimensional Direction of Arrival Estimation

In two-dimensional direction of arrival (2D-DOA) estimation, planar arrays can estimate the elevation and azimuth angles simultaneously. However, many planar array topologies such as billboard, L-shaped, T-shaped, and 2D nested arrays suffer from mutual coupling that results from the small separation between the physical sensors (antennas), which limits the estimation capability of the sensor array. In an attempt to reduce mutual coupling between sensors, this article proposes sparse billboard and T-shaped arrays in which the number of closely separated sensors is significantly reduced. In addition to extending the CRB for fourth order coarray, this article also derives closed-form expressions for the sensor locations and the number of consecutive lags or the uniform degrees of freedom (uDOF), in the fourth-order difference coarray (FODC). Simulation results demonstrate the robustness of the proposed sparse arrays against mutual coupling.

2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion.

In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains. We propose a covariance matrix completion algorithm for 2D-DOA estimation in a SUCA. The proposed algorithm estimates the complete covariance matrix of a fully sampled UCA (FUCA) from the sample covariance matrix of the SUCA through a neural network. Afterwards, the MUSIC algorithm is performed for 2D-DOA estimation with the completed covariance matrix. We conduct Monte Carlo simulations to evaluate the performance of the proposed algorithm in various scenarios; the performance of 2D-DOA estimation in the SUCA gradually approaches that in the FUCA as the SNR or the number of snapshots increases, which means that the advantages of a FUCA can be preserved with fewer RF chains. In addition, the proposed algorithm is able to implement underdetermined 2D-DOA estimation.

2D-DOA estimation with spatially separated “long” crossed-dipoles array based on three-way compressive sensing

2D-DOA estimation with spatially separated “long” crossed-dipoles array based on three-way compressive sensing

Evolutionary Heuristic Computing Paradigm for 2D-DOA Estimation along Circular Array

Direction of arrival (DOA) estimation problem has growing interest for the researcher investigating in system identification models arising in the field of digital signal processing, mobile communication, controls, and beamforming. In the presented work, evolutionary heuristic computing paradigm is presented for 2D-DOA estimation of plane waves impinging on uniform circular array. Performance metric of mean squared error is utilized as construction of a fitness function for the system, and the optimization strength of three methodologies, genetic algorithms (GAs), Pattern Search (PS), and integration of GAs with PS (GA-PS) is exploited for 2D-DOA estimation based on elevation as well as azimuth angles. Consistent precision, convergence, stability, and robustness of integrated heuristics of GA-PS are endorsed through outcomes of statistical observations.

An Algorithm for Single Snapshot 2D-DOA Estimation Based on a Three-Parallel Linear Array Model

An Algorithm for Single Snapshot 2D-DOA Estimation Based on a Three-Parallel Linear Array Model

2d Doa Estimation by a Large-Space T-Shaped Array

2d Doa Estimation by a Large-Space T-Shaped Array

2D-DOD and 2D-DOA Estimation Using Sparse L-Shaped EMVS-MIMO Radar

It is well-known that EMVS-MIMO radar is an emerging technique that it allows for 2D-DOD and 2D-DOA estimation. Unfortunately, existing array geometries on the EMVS-MIMO radar can rarely reach a good compromise between the estimation accuracy and the computational burden. This paper is aimed at proposing an L-shaped sparse array topology for a bistatic EMVS-MIMO radar, whose inter-element distance is much larger than half-wavelength. A fast algorithm proposed herein to estimate the 2D-DOD and 2D-DOA. Firstly, the direction cosine estimates are obtained via the rotational invariance properties of the sparse subarrays, which are ambiguous yet exhibit high-resolution. Thereafter, the direction cosine estimates are achieved via the vector cross-product of the normalized Poynting vectors, which are unambiguous but have low-resolution. The unambiguous high-resolution direction cosine estimates are determined by combining the previous results, following which the 2D-DOD and 2D-DOA can be easily recovered. It is shown that the proposed framework can obtain a better accuracy of estimation than the other existing methods. Moreover, it is more flexible than the current sparse array methodologies. Finally, the theoretical derivations have been validated by simulation results.

2D-DOA Estimation in Arc-Array With a DNN Based Covariance Matrix Completion Strategy

2D-DOA Estimation in Arc-Array With a DNN Based Covariance Matrix Completion Strategy

Recurrent NEAT Assisted 2D-DOA Estimation With Reduced Complexity for Satellite Communication Systems

Direction-of-arrival (DOA) estimation plays a vital role in the field of array signal processing. However, the need for heavy computing tasks in most traditional DOA algorithms, e.g., multiple signal classification (MUSIC), makes their engineering practicality significantly compromised in satellite communication systems. The neuroevolution of augmenting topologies (NEAT) can quickly search for appropriate topologies and weights of neural network functions, but its computational complexity is still too high for satellite systems. This paper proposes a modified NEAT architecture featuring a recurrent structure (RNEAT) that only needs a small number of phase components of the received signal covariance matrix as inputs to reduce the complexity and simplify the neural network architecture. The proposed RNEAT incorporated with multiple signal classification (RNEAT-MUSIC) features low complexity to achieve high resolution and low complexity simultaneously. Validation has been done by applying the proposed method in a two-dimensional direction of arrival estimation (2D-DOA) problem. Results show that the proposed RNEAT-MUSIC efficiently restricts the scanning region before forwarding the covariance matrix to the MUSIC stage. Consequently, the computational workload is reduced by 3/4 compared with the traditional 2D-MUSIC algorithm while maintaining satisfactory DOA resolution.