Direction Of Arrival Estimation Accuracy Research Articles

Cyclostationarity-based DOA estimation algorithms for coherent signals in impulsive noise environments

Estimating direction of arrival (DOA) is important in a variety of practical applications. Conventional cyclostationarity-based coherent DOA estimation algorithms are not robust to non-Gaussian α-stable impulsive noise. Additionally, fractional lower-order statistics (FLOS)-based algorithms are tolerant to impulsive noise; however, they experience performance degradation for coherent signals and interference. To overcome these drawbacks, two types of fractional lower-order cyclostationarity-based subspace DOA estimation methods are proposed for coherent signals in the presence of interference and α-stable impulsive noise. The new proposed algorithms exploit the fractional lower-order cyclostationarity properties of the signals and are immune to the impulsive noise and interference. Moreover, they can provide more accurate DOA estimates of coherent signals than conventional cyclostationarity-based and FLOS-based methods. The simulation results illustrate the robustness and effectiveness of the proposed methods for coherent signals based on a comparison with traditional methods. The new algorithms can be used in the presence of a wide range of interference, Gaussian noise, and α-stable distribution impulsive noise environments.

DOA Estimation of Unknown Emitter Signal Based on Time Reversal and Coprime Array.

In this paper, a novel direction-of-arrival (DOA) estimation for unknown (anonymous) emitter signal (ES) based on time reversal (TR) and coprime array (CA) is proposed. The resolution and accuracy of DOA estimation are enhanced from two aspects: one is from the view of array arrangement: the new distribution of CA is designed to reduce the holes, increase the degree of freedom (DOF) and apertures by rotating and translating only one subarray, which simplifies the operation. The other one is from the view of the algorithm: a neoteric DOA estimation algorithm with noise suppression based on TR, Capon and adaptive neuro-fuzzy inference system (ANFIS) is proposed for solving the wide sidelobe, multipath effect, low resolution and accuracy produced by conventional algorithms, in particular, those cannot work effectively under the existed hole condition. Furthermore, the resubmitting distorted noise and channel noise are suppressed effectively, which is not taken into considered in the conventional Capon algorithm. Simulation results including the resolution, accuracy, root mean square error (RMSE), Cramér-Rao lower bound (CRLB) and the compared analyses on uniform linear array (ULA), nested array (NA) and minimum redundancy array(MRA) demonstrate the performance advantages of the proposed DOA estimation algorithm even at very low signal-to-noise ratio (SNR) condition.

Distributed Maximum Likelihood DOA Estimation Algorithm for Correlated Signals in Wireless Sensor Network

The performance of the existing direction of arrival (DOA) estimation algorithms for source localization in the wireless sensor networks (WSN) degrades when the sources are correlated. The degradation is due to the rank deficiency of the source covariance matrix which is reflected in the high cramer-Rao lower bound (CRLB) for correlated sources. Unlike subspace based techniques, maximum likelihood (ML) based technique does not require any preprocessing technique for DOA estimation of correlated signals. Hence ML technique can be directly applied for WSN with arbitrary array geometry. The DOA estimation accuracy for correlated signals is improved by employing distributed ML approach in this paper. The subarray formed at a particular node which is experiencing highest CRLB can improve its estimation accuracy by receiving better DOA estimates from its neighbors. The corresponding distributed CRLB is derived and found a substantial improvement in the distributed scenario. The distributed CRLB lies between that of local CRLB for the subarray formed at the node and global CRLB for the array formed by all sensors in the WSN. Diffusion quantum particle swarm optimization is used to optimize ML estimator for fully correlated signals as it has only a single parameter for tuning. Simulation results show that the estimation accuracy improves at a node even for correlated signals yielding highest CRLB using distributed approach. The root mean square error at a specific node using distributed algorithm approaches to the derived distributed CRLB.

Two-dimensional multiple-snapshot grid-free compressive beamforming

Abstract Compressive beamforming with planar microphone arrays can effectively achieve the two-dimensional direction-of-arrival (DOA) estimation and strength quantification of acoustic sources. This paper focuses on developing a multiple-snapshot grid-free method and comparing it against the existing grid-based and single-snapshot grid-free ones. The minimization of the atomic norm of multiple-snapshot microphone pressure induced by acoustic sources and the matrix pencil and pairing method are utilized jointly. The former is utilized to denoise the measured pressure and therefore obtain the pressure from acoustic sources. The latter is utilized to estimate DOAs and quantify strengths. Both simulations and experiments demonstrate the advantages of the developed method. For sources that are active across multiple snapshots, it can overcome the basis mismatch conundrum of the grid-based compressive beamforming. When the sources are uncorrelated or partially correlated in both spatial and temporal dimensions, it outperforms the single-snapshot grid-free compressive beamforming in terms of denoising, DOA estimation and strength quantification accuracy and resolution. These advantages stand up whether a standard uniform rectangular array or a non-uniform array constituted by a small number of microphones is utilized.

Accurate DOA Estimation for Large-Scale Uniform Circular Array Using a Single Snapshot

A large-scale antenna array is an enabling technique for millimeter-wave communications. Uniform circular arrays (UCAs) have the spatial invariance property, ensuring the same beamforming performance in the whole angular region. However, the direction-of-arrival (DOA) estimation in UCAs is challenging since the array response of a UCA does not conform to a Vandermonde structure as that of a uniform linear array. This letter proposes an accurate and low-complexity DOA estimation approach by exploiting the good correlation property of the array response of the UCA. The DOA estimates are first obtained from a circular convolution between a single snapshot and the designed coefficient vector. Then, by searching for the best initial phase of the coefficient vector, the DOA estimates can be refined to a configurable accuracy. The simulation results demonstrate that the proposed approach outperforms the state of the art by orders of magnitude in estimation accuracy.

A Novel Designed Sparse Array for Noncircular Sources with High Degree of Freedom

The existing coprime array is mainly applicable to circular sources, while the virtual array degree of freedom (DOF) for noncircular sources is enhanced limitedly. In order to perfect the array DOF and the direction of arrival (DOA) estimation accuracy, a high degree of freedom sparse array design method for noncircular sources is put forward. Firstly, the method takes the advantages of the characteristic of the noncircular sources to expand the array manifold and then explores and solves the location distribution of the physical array sensors on the basis of the virtual array model with the help of the searching approach. The array configuration can obtain the longest continuous virtual array. The comparisons between the proposed array configuration and the common array configurations are advanced. The simulation experiments show that the sparse array presented in this paper can effectively increase the continuous virtual array aperture of noncircular sources, improve the array DOF and DOA estimation accuracy, and achieve the purpose of better estimation of multiple DOAs in underdetermined conditions.

DOA estimation method for multi‐path targets based on TR MIMO radar

To reduce the large errors for direction of arrival (DOA) estimation in the multipath condition, a DOA estimation method for multipath targets based on time reversal (TR) multi-input multi-output (MIMO) radar is proposed. First, the proposed algorithm combines the focusing characteristic of TR technology with the waveform diversity and symmetry characteristic of MIMO radar, obtaining the virtual sub-arrays of echo signal. Then, the authors multiplex the rows and columns and apply forward–backward spatial smoothing algorithm to remove the coherence of the virtual sub-arrays, effectively improving the DOA estimation accuracy for multipath targets. Simulation results verify the validity of the proposed method.

High-Accuracy and Low-Complexity DOA Estimation Algorithm for Transmit-Only Diversity Bistatic MIMO Radar

This paper proposes TOD-MUSIC algorithm to improve the Direction of Arrival (DOA) estimation accuracy under low Signal-to-Noise Ratio (SNR) in transmit-only diversity bistatic Multiple-Input Multiple-Output (MIMO) radar. Firstly, the TOD-MUSIC algorithm uses Toeplitz matrix reconstruction to make it possible to process the correlated sources. Then it uses the linear operation to reduce the computational complexity. Finally, the physical properties of spectral peak (which are not continuous functions and approach a larger value at the DOAs) is utilized to make the spectral peak more obvious. It is proved that the TOD-MUSIC algorithm can effectively improve the DOA estimation accuracy. We reveal that the TOD-MUSIC algorithm can not only process correlated sources, but also reduce calculation amount by solving the DOA estimation ambiguity problem under the under-sampling. The simulation results show that: the TOD-MUSIC algorithm makes the DOA estimation accuracy improved by 2° at low SNR, approaching to MUSIC algorithm at high SNR.

Accurate estimation of DOA and MAI mitigation in the presence of channel impairments at low SNR

Environmental effects may cause antenna array element positions to become disturbed, introducing array shape uncertainties. The presence of these uncertainties causes a rapid degradation in the detection, resolution and estimation performance of array processing algorithms. In this paper, the performance of the Multiple Signal Classification - Direction of Arrival (DOA) algorithm for estimating the direction of the incident angles of all the received signals impinging on the antenna array elements is analysed at low Signal to Noise Ratio (SNR). The investigation is achieved under different scenarios such as changing the number of antenna array elements at different values of SNR. The root mean square error of the estimated angles is computed and compared with the true angles of the desired user. The simulation results assure that in the presence of the reference signal, the DOA algorithm can identify the direction of the desired user with high accuracy and high resolution. Moreover, the Affine Projection Algorithm (APA) is implemented to enhance the accuracy of the estimated direction in order to generate a main beam towards the desired user and null towards interfering users.

Accurate estimation of DOA and MAI mitigation in the presence of channel impairments at low SNR

Environmental effects may cause antenna array element positions to become disturbed, introducing array shape uncertainties. The presence of these uncertainties causes a rapid degradation in the detection, resolution and estimation performance of array processing algorithms. In this paper, the performance of the Multiple Signal Classification - Direction of Arrival (DOA) algorithm for estimating the direction of the incident angles of all the received signals impinging on the antenna array elements is analysed at low Signal to Noise Ratio (SNR). The investigation is achieved under different scenarios such as changing the number of antenna array elements at different values of SNR. The root mean square error of the estimated angles is computed and compared with the true angles of the desired user. The simulation results assure that in the presence of the reference signal, the DOA algorithm can identify the direction of the desired user with high accuracy and high resolution. Moreover, the Affine Projection Algorithm (APA) is implemented to enhance the accuracy of the estimated direction in order to generate a main beam towards the desired user and null towards interfering users.

Off-Grid DOA Estimation for Colocated MIMO Radar via Reduced-Complexity Sparse Bayesian Learning

Recent advance on signal processing has witnessed increasing interest in machine learning. In this paper, we revisit the problem of direction-of-arrival (DOA) estimation for colocated multiple-input multiple-output (MIMO) radar from the perspective of machine learning. The reduced-complexity transformation is first applied on the array data from matched filters, thus eliminating the redundancy of the array data for the relief of calculational burden. Furthermore, the pre-whitening is followed to obtain a simplified noise model. Finally, the DOA estimation is linked to off-grid sparse Bayesian learning (OGSBL), which does not require to update the noise hyper-parameter, and a block hyper-parameter is utilized to accelerate the convergence of the OGSBL algorithm. The proposed estimator provides better DOA estimation accuracy than the existing peak searching algorithm. The effectiveness of the proposed algorithm is verified via numerical simulation.

Blind DOA Estimation in a Reverberant Environment Based on Hybrid Initialized Multichannel Deep 2-D Convolutional NMF With Feedback Mechanism

The accuracy performance of traditional direction of arrival (DOA) estimation algorithms is seriously affected by the reverberation. Considering the advantage of the sparse characteristic of speech signal in time-frequency (T-F) domain, this paper presents a new blind DOA estimation method based on integrated deep learning and convolutional non-negative matrix factorization (NMF). Firstly, mathematic models of microphone array and room impulse response are built. In addition, we extracted blindly initialization parameters of 2-D convolutional NMF using k-means clustering algorithm and singular value decomposition algorithm, which can be used to accurately estimate the main components of desired sound source in the reverberation environment of multi-path propagation. Moreover, the feedback mechanism is introduced into deep 2-D convolutional NMF and correlation coefficient between the signal decomposed by NMF and the signal to be decomposed is used to select the best separated signal for DOA estimation, which make the separation algorithm simpler and more efficient. Finally, test of orthogonality of projected subspaces (TOPS) algorithm is used to validate the DOA estimation capability of this algorithm. Compared with the unprocessed reverberation speech, the estimation error is reduced, which shows that the proposed algorithm can effectively improve the estimation accuracy of DOA estimation when the received signals are in a reverberant environment.

Real-Valued Near-Field Localization of Partially Polarized Noncircular Sources With a Cross-Dipole Array

In this paper, a novel near-field localization algorithm is proposed for partially polarized noncircular sources. The algorithm is composed of two stages. In the first stage, based on the cross-dipole, the array observations are transformed into the real-valued domain. Then, an extended covariance matrix is constructed and the structure of the real-valued steering vector is utilized to factorize the direction of arrival (DOA) parameter from other parameters. Based on the rank reduction (RARE) principle, the DOA parameters can be obtained through the one-dimensional (1-D) spectral search. In the second stage, with the DOA estimates, the range parameters are generated via another RARE estimator, which also only requires a 1-D spectral search. Compared with the conventional second-order statistics (SOS)-based methods, the proposed algorithm is computationally more efficient since it only requires 1-D spectral search and real-valued computations. Moreover, it can resolve more sources and promote the DOA estimation accuracy. Numerical simulation results corroborate the effectiveness and efficiency of the proposed method.

Underdetermined Direction of Arrival Estimation of Non-Circular Signals via Matrix Completion in Nested Array

Direction-of-arrival (DOA) estimation using nested linear array ignores the information from repeated sensors, thus involves the problem of losing accuracy. Moreover, non-circular feature of signals is rarely considered and it results in discontinuity of the virtual array, namely, holes appear. This paper first provide an improved data averaging method to increase the accuracy of DOA estimation by fusing data from covariance and elliptic covariance. And then an algorithm based on matrix completion theory in nested array that greatly extends the degrees of freedom (DOF) and is able to find more sources than number of physical sensors is presented. The algorithm reconstructs the covariance matrix of the virtual linear array, which exactly has the same shift invariance as the uniform linear array but a higher aperture. Considering about the missing elements of the virtual array covariance matrix, we apply matrix completion theory to solve the problem. Finally, true DOAs of multiple signals can be obtained through subspace algorithms. Numerical results demonstrate that the proposed algorithms can obtain high accuracy while underdetermined DOA estimation is realized.

An Improved Propagator Estimator for DOA Estimation in Monostatic MIMO Radar in the Presence of Imperfect Waveforms

This paper discusses the direction-of-arrival (DOA) estimation problem in multiple-input multiple-output (MIMO) radar with non-orthogonal waveforms. Unlike the traditional orthogonal waveforms-based counterparts, spatially colored noise as well as corrupted virtual direction matrix would appear due to the correlated waveforms in DOA estimation. To tackle these issues, an improved propagator estimator is developed. The spatially colored noise caused by waveform imperfectly is eliminated via cross-correlation technique. Thereafter, DOA is obtained by exploiting the propagator and invariant techniques. The proposed estimator does not involve eigendecomposition of the high dimensional matrix, and it provides closed-form solution for DOA estimation. Compared with the existing frameworks, it is computationally much more economic. Moreover, it offers very close DOA estimation accuracy to the matrix-based methods. Numerical simulations are carried out to show the improvement of the proposed estimator.

Low-Profile ESPAR Antenna for RSS-Based DoA Estimation in IoT Applications

In this paper, we have introduced a low-profile electronically steerable parasitic array radiator (ESPAR) antenna that can successfully be used to estimate the direction-of-arrival (DoA) of incoming signals in wireless sensor network (WSN) applications, in which the height of the complete antenna has to be low. The proposed antenna is over three times lower than high-profile ESPAR antenna designs currently available in the literature for the DoA estimation; it can provide eight unique main beam directions and relies on simplified beam steering, which makes it applicable to simple and inexpensive WSN nodes. Measurements using our fabricated ESPAR antenna prototype indicate that relying solely on the received signal strength values recorded at the antenna output port, it is possible to achieve accurate DoA estimation results with error levels similar to those available for high-profile ESPAR antennas relying on the similar energy-efficient simplified beam steering concept and having 12 unique main beam directions. As a consequence, the overall time required for the DoA estimation using the proposed antenna can be reduced by 33%.

A PARAFAC Decomposition Algorithm for DOA Estimation in Colocated MIMO Radar With Imperfect Waveforms

In this paper, we focus on the problem of direction-of-arrival (DOA) estimation for a colocated multiple-input multiple-output radar with imperfect waveforms, and a parallel factor (PARAFAC)-based algorithm is proposed. First, the spatial cross-correlation technique is adopted to eliminate the spatially colored noise caused by the nonorthogonal waveforms. To utilize the inherent tensor structure of the array data, the covariance matrix is rearranged into a fourth-order PARAFAC decomposition model. Thereafter, a quadrilinear decomposition algorithm is developed, which obtain the direction matrices via alternating least squares strategy. Finally, the DOAs are achieved through solving a least squares fitting problem. The proposed scheme does not require the prior knowledge of the waveform correlation matrix, and it is computationally more efficient than the state-of-the-art matrix completion (MC) approach. Furthermore, the proposed method may offer more accurate DOA estimation performance than the MC approach. The numerical experiments are provided to show the improvement of our algorithm.

Iterative Reweighted DOA Estimation for Impulsive Noise Processing Based on Off-Grid Variational Bayesian Learning

The performance of acoustic source localization with array system is limited by impulsive noise such as electromagnetic interference, car ignitions, bursting, and so on. The impulsive noise decays with heavy-tailed distribution which can be considered as outliers. In order to alleviate the performance degradation of traditional direction-of-arrival (DOA) estimation with impulsive noise, a novel iterative reweighted variational Bayesian learning algorithm based on off-grid model (OG-WVBL) is proposed. OG-WVBL employs impulsive noise as two independent components and models directly the outliers with sparse distribution in the time domain. OG-WVBL utilizes two iterative VBL to reconstruct signal and outliers matrix and then retrieves the DOA. Then, OG-WVBL also introduces the iteratively reweighted strategy to hyperparameters so that the more importance is given to those hyperparameters with non-zero entries over others which can encourage sparsity and achieve the consistent convergence. With the iteratively reweighted strategy, OG-WVBL can automatically identify the number of sources without any prior knowledge. Moreover, the proposed algorithm can use a coarse sampling grid to achieve the accurate DOA estimation with the off-grid model. The experiments and simulation results show that OG-WVBL possesses robust performance and outperforms several existing algorithms under impulsive noise environment.

An Off-Grid DOA Estimation Method Using Proximal Splitting and Successive Nonconvex Sparsity Approximation

Direction-of-arrival (DOA) estimation is a fundamental problem in many signal processing. Recently, a variety of sparsity-aware methods have been proposed for DOA estimation. The discrimination of grid in these methods often suffers from grid mismatch problem, which degrades the performance of DOA estimation, when the true unknown DOAs are not on the potential angular grids. To deal with grid mismatch problem in conventional sparsity-based methods, a novel off-grid model is proposed via Taylor interpolation that jointly estimates the sparse signals and grid offset parameters, using a family of successive nonconvex sparsity approximation penalties on the sparse signals. Then an iterative alternating optimization strategy is utilized to tackle this nonconvex problem with respect to sparse signals and grid offset parameters, and the proposed algorithm is solved by the proximal splitting algorithm in each iteration, where the optimization problem is split into two parts: sparsity and projection. The proposed framework in this paper is universal and also can be extended to other off-grid sparsity-based algorithms. The extensive simulations are conducted to verify that the proposed method achieves superior resolution and more accurate DOA estimation performance than other conventional sparsity-based method and the state-of-the-art off-grid methods in many cases of practical interest.

Joint DOA and Polarization Estimation with Two Parallel Sparse Dipole Arrays

Electromagnetic vector sensors (EMVS) have attracted growing attention in recent years. However, the mutual coupling effects in practical EMVS arrays may seriously degrade the parameter estimation performance. In order to solve this problem, a novel array configuration consisting of two parallel sparse dipole arrays is proposed. Based on the spatially rotational invariance property between the two parallel arrays and the interdipole spacing inside each array, highly accurate but ambiguous direction-cosine estimates, coarse direction-of-arrival (DOA) estimates, and polarization parameter estimation can be obtained jointly. The coarse DOA estimates are then employed to disambiguate the phase ambiguities in the fine estimates. Compared with collocated EMVS, the proposed array overcomes the mutual coupling problem. Moreover, the DOA estimation accuracy is promoted due to the sparse array aperture extension. Simulation results demonstrate the effectiveness of the proposed algorithm.