Direction Of Arrival Estimation Of Signals Research Articles

Cumulant-based DOA Estimation of Noncircular Signals Against Unknown Mutual Coupling.

To effectively find the direction of non-circular signals received by a uniform linear array (ULA) in the presence of non-negligible perturbations between array elements, i.e., mutual coupling, in colored noise, a direction of arrival (DOA) estimation approach in the context of high order statistics is proposed in this correspondence. Exploiting the non-circularity hidden behind a certain class of wireless communication signals to build up an augmented cumulant matrix, and carrying out a reformulation of the distorted steering vector to extract the angular information from the unknown mutual coupling, by exploiting the characteristic of mutual coupling, i.e., a limited operating range and an inverse relation of coupling effects to interspace, we develop a MUSIC-like estimator based on the rank-reduction (RARE) technique to directly determine directions of incident signals without mutual coupling compensation. Besides, we provide a solution to the problem of coherency between signals and mutual coupling between sensors co-existing, by selecting a middle sub-array to mitigate the undesirable effects and exploiting the rotation-invariant property to blindly separate the coherent signals into different groups to enhance the degrees of freedom. Compared with the existing robust DOA methods to the unknown mutual coupling under the framework of fourth-order cumulants (FOC), our work takes advantage of the larger virtual array and is able to resolve more signals due to greater degrees of freedom. Additionally, as the effective aperture is virtually extended, the developed estimator can achieve better performance under scenarios with high degree of mutual coupling between two sensors. Simulation results demonstrate the validity and efficiency of the proposed method.

MT-BCS-Based DoA and Bandwidth Estimation of Unknown Signals through Multiple Snapshots Data

The Direction-of-Arrival (DoA) and bandwidth (BW) estimation strategy impinging on a linear array using multiple snapshots data is addressed within the multitask Bayesian Compressive Sensing (MT-BCS). The DoA estimation is used as the reconstruction of sparse signal constrained by the Laplace prior through multitask Bayesian Compressive Sensing. Receiving wideband signal data through linear array, the space is divided into I parts according to the equal interval. The data of interest are assumed to be represented as I-dimensional vector, and the wideband signal can be reconstructed accurately using only a small number M. The receiving antenna operates in the frequency range fmin,fmax. Starting from the voltages measured at the output of the array elements at a multiple time instants at fp=fmin+Δf,p=1,…,P, the retrieval of the DoAs is addressed by means of a customized strategy based on MT-BCS in order to correlate the solutions obtained over different frequency samples. The bandwidth of the signals is obtained as a byproduct by identifying at which frequencies the MT-BCS estimations include a signal along the ith (i = 1,…, I) sampling direction. From the outputs of different frequencies, we can know the DoA and BW of signals. A preliminary numerical result is reported to show the behavior of the proposed approach in multiple snapshots data.

Low-Complexity Joint 2-D DOA and TOA Estimation for Multipath OFDM Signals

The multiple signal classification (MUSIC) algorithmis computationally expensive in the application to joint two-dimensional (2-D) direction-of-arrival (DOA) and time-of-arrival (TOA) estimation based on uniform circular array (UCA) using orthogonal frequency-division multiplexing (OFDM) signal. This letter proposed an efficient way to compute the 3-D spatial-temporal spectrum. We extended the manifold separation technique, by which we obtained the 3-D discrete Fourier transform (DFT) form of the spectrum. On this basis, we proposed two 2-D DOA and TOA estimators called FFT-MUSIC and Two-step FFT-MUSIC. The former computed the spectrum by large size fast Fourier transform (FFT) to reduce the grid error in searching DOAs and TOAs. The latter roughly located the DOAs and TOAs by relatively small size FFT, followed by the subspace-based technique for a fine-grained searching within a local grid. Simulation results showed that both estimators can reduce the computation cost by one to two orders of magnitude, as compared to their conventional counterparts, while maintaining a similar accuracy.

Maximum likelihood approach to DoA estimation using lens antenna array

Massive antenna array has been proposed to improve the spectral efficiency and link reliability in wireless communication systems. However, using large antenna arrays incurs additional cost in terms of signal processing and hardware complexity. The electromagnetic (EM) lens-focusing antennas are introduced as a promising technique to reduce the hardware complexity and cost. On the other hand, determining the location of users in terms of their direction-of-arrival (DoA) using these lens array becomes of great interest for different 5G services. This paper addresses the issue of DoA estimation by adopting lens antenna array (LNA). We firstly derive an expression for the received signal with the adoption of LNA, and then a maximum likelihood (ML) estimator for the DoA has been obtained. Depending on the ability of the lens array to focus the signal power on a subset of antennas as a function of DoA. We propose using the antenna selection (AS) technology to select an antenna subset aiming to reduce the number of radio frequency (RF) chains and accordingly reducing the hardware cost. The simulation results show the the capability of the proposed method to avoid the phase ambiguity problem and provide high accurate DoA estimation of signals.

A Robust Direction of Arrival Estimation Method for Uniform Circular Array.

Estimating the Direction of Arrival (DOA) is a basic and crucial problem in array signal processing. The existing DOA methods fail to obtain reliable and accurate results when noise and reverberation occur in real applications. In this paper, an accurate and robust estimation method for estimating the DOA of sources signal is proposed. Incorporating the Estimating Signal Parameters via Rotational Invariance Techniques (ESPRIT) algorithm with the RANdom SAmple Consensus (RANSAC) algorithm gives rise to the RAN-ESPRIT method, which removes outliers automatically in noise-corrupted environments. In this work, a uniform circular array (UCA) is converted into a virtual uniform linear array (ULA) to begin with. Then, the covariance matrix of the received signals of the virtual linear array is reconstructed, and the ESPRIT algorithm is deployed to estimate initial DOA of the source signal. Finally, the modified RANSAC method with automatically selected thresholds is used to fit the source signal to obtain accurate DOA. The proposed method can remove the unreliable DOA feature data and leads to more accuracy of DOA estimation of source signals in reverberation environments. Experimental results demonstrate that the proposed method is more robust and efficient compared to the traditional methods (i.e., ESPRIT, TLS-ESPRIT).

Wideband signal DOA estimation method based on keystone transform

Here, a wideband signal direction-of-arrival (DOA) estimation method was proposed. By applying the keystone transform, the phase difference caused by different frequency is eliminated, and then the narrowband DOA estimation method can be used directly. Compared with the traditional method, the proposed approach can achieve better estimation accuracy than incoherent subspace method and coherent subspace method at low signal-to-noise ratio. The theoretical derivation of the method is given. Monte–Carlo simulations are provided to demonstrate the performance of the proposed scheme.

Design of a four‐element array for accurate direction of arrival estimation in phase interferometry systems

AbstractThis article proposes the design of a four‐element array antenna for the accurate direction of arrival (DOA) estimation of Global Positioning System (GPS) interference signals. The proposed array antenna has four individual antenna elements, and each element consists of a radiating patch on the upper layer and a feeding loop on the lower layer. The patch and loop are intentionally designed in a circular shape to obtain the symmetric current distributions on the radiators. The feeding loop is directly connected by two output ports of an external hybrid chip coupler, and the radiating patch is then electromagnetically coupled by near‐fields of the feeding loop. This feeding mechanism in the proposed structure allows for a more uniform current and H‐field distribution for phase characteristics of the radiation that are close to the ideal isotropic radiators in the azimuth angle. In addition, the high‐dielectric ceramic substrate is adopted to minimize the physical height of the antenna for the phase characteristics of the radiation close to the ideal isotropic radiators in elevation angles. To validate that the phase characteristics are similar to those of an ideal radiator, the DOA estimation performances are observed using a dual‐axis interferometry. The results demonstrate that the proposed four‐element array antenna with phase difference characteristics similar to those of an ideal radiator is suitable for the accurate DOA estimation of GPS interference signals.

CFAR Block-Sparse Bayesian Learning Algorithm for the Off-grid DOA Estimation with Coprime Array

Constant false-alarm rate Block-sparse Bayesian learning (CFAR-BSBL) algorithm is proposed to reduce the computational complexity and improve Direction of arrival (DOA) estimation accuracy of offgrid signals with coprime array. Firstly, a signal model with normalized noise is built to avoid the learning procedure of noise parameter. Secondly, a block sparse Bayesian framework is built with the introduction of a temporary correlation matrix in order to use t he temporal structure of incident signals. Then the algorithm uses CFAR detection to detect the grids close to the real DOA and relieve the dependence on the number of signals. Finally, an off-grid process based on the closest grids is adopted to deal with the off-grid problem. The proposed CFAR-BSBL algorithm can obtain high accuracy and low complexity DOA estimation of off-grid signals with coprime array.

Estimation of Interference Arrival Direction Based on a Novel Space-Time Conversion MUSIC Algorithm for GNSS Receivers.

In the estimation of the direction of arrival (DOA) for interference signals, the direction-finding error of the multiple signal classification (MUSIC) algorithm will increase in the case of multiple interferences or when the interfering signal power is weak. In this paper, a space-time conversion MUSIC (STC-MUSIC) algorithm is proposed, and the concept of a focusing parameter is introduced to improve the performance of the DOA estimation. Meanwhile, a method of variable step size peak search is proposed to reduce the amount of calculation of the STC-MUSIC algorithm. The final simulation and experimental results show that the STC-MUSIC algorithm improves the purity of the noise subspace effectively, thus improving the precision and robustness of the DOA estimation for interference signals significantly. In comparison to traditional algorithms, the convergence, stability, root mean square error (RMSE) and other performance characteristics are improved greatly.

Two‐dimensional DOA estimation for coherent signals using a novel covariance‐like matrix

AbstractThis paper addresses a two‐dimensional (2‐D) direction of arrival (DOA) estimation of coherent signals problem. To do so, we first reconstruct a covariance‐like matrix, and then apply forward and backward spatial smoothing (FBSS) method as a preprocessor to remedy the rank deficiency problem. By utilizing the covariance‐like matrix instead of outputs sensors in the FBSS method, the noise and small fading factor influences are reduced in the DOA estimation problem. Afterward, two matrix pencil pairs are exploited to extract 2‐D DOA of impinging signals. This two matrix pencil pairs method can estimate 2‐D DOA with repeated eigenvalues efficiently and also pair the estimated parameters automatically. The proposed algorithm not only has all the privileges of the estimation of signal parameters via rotational invariant techniques (ESPRIT)‐like algorithm but also increases the array aperture. By mathematical tractability, we derive Cramer‐Rao bounds for azimuth and elevation angles for coherent signals received by uniform rectangular array to compare the performance of the estimation algorithms with a lower bound. Simulation results show that the proposed algorithm outperforms the symmetric configuration of uniform rectangular array (SC‐URA), the FBSS–multiple‐signal classification (FBSS‐MUSIC) and the ESPRIT‐like algorithms in terms of root mean squared error (RMSE).

DOA estimation of multipath signals in the presence of gain‐phase errors using an auxiliary source

In this paper, we address the problem of direction‐of‐arrival (DOA) estimation of multipath signals in the presence of gain‐phase uncertainties using an auxiliary source (AS) with uniform rectangular array. Based on the assumption that the waveform of the AS is a known prior, and the signal of the AS impinges on the array without multipath propagation, a two‐step algorithm is proposed to estimate the DOAs along with the fading coefficients and gain‐phase errors. First, a coarse estimation is obtained by extending the traditional spatial smoothing‐based ESPRIT algorithm. Second, in order to overcome the performance deterioration due to a reduction of the effective array aperture, an alternate iterative algorithm is proposed. Meanwhile, the relevant stochastic Cramér‐Rao bound (CRB) is also derived. Simulation results show that the proposed method is capable of approaching the stochastic CRB when the signal–noise ratio (SNR) of the AS is large enough and is greater than the SNR of the unknown source. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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.

Aligned Propagator Scanning Approach to DOA Estimation of Circular and Noncircular Wideband Source Signals

The problem addressed in this paper is focused on the direction-of-arrival (DOA) estimation of the circular and noncircular wideband signals. The propagator scanning scheme is applied to the array output augmented cross covariance matrix by aligning first the baseband array observation. When the scanning angle equals a true DOA of signal, the complex envelope of the corresponding signal is perfectly aligned, and then, the associated propagator can be used to fulfill subspace separation based wideband direction finding. This method involves no data focusing and eigendecomposition. The performance of the proposed method is analyzed in terms of the mean-square errors of the DOA estimates. Simulation results are included to evaluate the new technique and to verify the performance analysis.

2-D DOA Robust Estimation of Echo Signals Based on Multiple Satellites Passive Radar in the Presence of Alpha Stable Distribution Noise

In this paper, the low-performance problem of two-dimensional (2-D) direction of arrival (DOA) estimation with non-Gaussian noise in low signal-to-noise ratio is addressed. For echo signals of the multiple satellites’ passive radar, a robust 2-D DOA estimation method based on improved zero-order statistics fractional low-order cyclic correlation (IZOS-FLOCC) under the alpha-stable distribution noise environment is proposed. First, we employ a uniform plane array to establish the model of received signals. Then, the IZOS-FLOCC of the echo signals is constructed based on the cyclostationarity. Finally, the IZOS-FLOCC of signals subspace is obtained by the optimization method. Moreover, we derive the Cramer–Rao low bound of the 2-D DOA estimation of echo signals based on multiple satellites’ passive radar in the presence of alpha-stable distribution noise. The simulation results show that the proposed method can effectively estimate the 2-D DOA of echo signals based on multi-satellite radiation sources in the alpha-stable distribution noise environment and achieve better performance than the existing methods.

Sparsity-Inducing DOA Estimation of Coherent Signals Under the Coexistence of Mutual Coupling and Nonuniform Noise

The sparse reconstruction techniques can improve the accuracy and resolution of the direction of arrival (DOA) estimation using sensor arrays. However, due to reflective objects and nonidealities of the antennas and circuitry, the received signals may be coherent and coupled to each other in nonuniform noise environments, causing severe performance degradation of the signal sparse reconstruction. In this paper, a novel sparsity-inducing DOA estimation method is proposed to adapt to such a challenging scenario. To mitigate the nonuniform noise, its power components are first eliminated by a linear transformation. Then, leveraging the steering vector parametrization based on the banded symmetric Toeplitz structure of the mutual coupling matrix (MCM), a reweighted l 1 -norm minimization subject to an error-constrained l 2 -norm is designed to determine the DOA estimates, further enhancing the sparsity and providing robustness against the noise. In addition, a new stochastic Cramer-Rao lower bound (CRLB) of the DOA estimation is derived for the considered adverse condition. The simulation results demonstrate the superiority of the proposed method over its state-of-the-art counterparts.

Unitary Matrix Completion-Based DOA Estimation of Noncircular Signals in Nonuniform Noise

In this paper, a novel direction-of-arrival (DOA) estimation algorithm is proposed for noncircular signals with nonuniform noise by using the unitary matrix completion (UMC) technique. First, the proposed method utilizes the noncircular property of signals to design a virtual array for approximately doubling the array aperture. Then, the virtual complex-valued covariance matrix with the unknown nonuniform noise is transformed into the real-valued one by utilizing the unitary transformation to improve the computational efficiency. Next, a novel UMC method is formulated for the DOA estimation to remove the influence of nonuniform noise. Finally, the DOA without the influence of the unknown noncircularity phase is obtained by using the modified estimation of signal parameters via rotational invariance technique (ESPRIT). Especially, for handling the coherent sources, the forward–backward spatial smoothing technique is utilized to reconstruct a full-rank covariance matrix so that the signal subspace and the noise subspace can be correctly separated. Due to utilizing the extended array aperture and the unitary transformation, the proposed method can identify more sources than the number of physical sensors and provides higher angular resolution and better estimation performance. Compared with the existing DOA estimation algorithms for noncircular signals, the proposed one can effectively suppress the influence of the nonuniform noise. The simulation results are provided to verify the effectiveness and superiority of the proposed method.

Weighted Incoherent Signal Subspace Method for DOA Estimation on Wideband Colored Signals

Wideband direction-of-arrival (DOA) estimation is a key part in array signal processing. The existing algorithms for the wideband DOA estimation are often studied in the situation of uniformly distributed energy. In addition, all the frequency bins are weighted equally in these algorithms. However, these algorithms perform unsatisfactorily when encountering wideband colored signals with nonuniform energy spectrum. To improve the performance of DOA estimation for wideband colored signals, we proposed two weighting methods, which are based on the perturbed subspace theory and random matrix theory, respectively. The two methods weight the space spectrum from all the frequency bins according to the mean square error of DOA estimation in each frequency bin. The numerical results show that the random matrix theory based method performs well, due to the inference premise that the dimensions of matrices increase at the same rate. The perturbed subspace-based method, which is concise in calculating the weights, shows high accuracy only at high-signal-to-noise ratio and with adequate snapshots. The effectiveness of the two algorithms are also demonstrated by comparing them to various existing algorithms and the Cramer–Rao bound.

Direction of Arrival Estimation of Uncorrelated Signals Using Root-MUSIC Algorithm for ULAs and UCAs

In this paper, Root-MUSIC algorithm for direction of arrival (DOA) estimation of uncorrelated signals is explored both for uniform linear and uniform circular arrays. The basic problem in Uniform Linear Arrays (ULAs) is Mutual coupling between the individual elements of the antenna array. This problem is reduced in Uniform Circular Arrays (UCAs) because of its symmetric structure. The DOA estimation of uncorrelated signals that have different power levels is simulated on a MATLAB environment. And the noise consider is white across all the array elements. The factors considered for simulation are number of number of snapshots, array elements, radius of circular array, array length, and signal to noise ratio.

Efficient DOA estimation of noncircular signals in the presence of multipath propagation

The noncircularity of uncorrelated signals can improve the performance of direction of arrival (DOA) estimation using antenna arrays. However, due to reflection from surfaces or malicious jamming, the received noncircular signals may be a mixture of uncorrelated and coherent components in practice, which has not been considered by existing algorithms. This paper addresses the issue of DOA estimation for noncircular signals under multipath fading in smart antenna systems, and a novel two-stage processing method is proposed accordingly. By expanding the signal subspace from augmented array output, the DOA estimates of uncorrelated sources are first obtained, and then the covariance matrix of uncorrelated signals is reconstructed and removed from that of the mixed signals afterwards. Secondly, a spatial smoothing technique is performed on a denoised covariance to restore rank deficiency in the covariance of the remaining coherent signals. The resultant algorithm can resolve more signals than array elements with higher accuracy and better classification of signal type than previous methods. Additionally, a new deterministic direction estimation Cramér–Rao lower bound (CRLB) is derived for the considered mixture model. Simulation results demonstrate the validity and efficiency of the proposed method.

Methods of suppressing tow ship noise with a horizontal linear array

Tow ship noise seriously influence the detection and direction of arrival (DOA) estimation performance of towed array. Null steering beamforming(NSBF), inverse beamforming(IBF) and non-data based beam-space transformation(NDBBT) are applied to suppress interference of tow ship in this paper. These methods are evaluated according to the improvement of signal interference noise ratio and DOA estimation precision. Simulation and experimental results show that all three methods can suppress interference of tow ship effectively so as to improve DOA estimation precision. In addition, IBF and NDBBT do better in suppressing interference than NSBF.