High resolution direction of arrival estimation methods applied to measurements from a digital array antenna

In this paper we propose for comparative analysis of wideband direction of arrival (DOA) estimation methods with nested array. Previously developed methods, Incoherent method, Coherent signal subspace processing (CSSM) and Weighted average of signal subspace (WAVES) could not perform satisfactory DOA estimation due to the single dimensionality and associated with lesser degree of freedom. In nested array, DOA estimation is done on the same source that is Gaussian source with additive white Gaussian noise (AWGN). While, nested array performed DOA estimation by decomposing uniform linear array (ULA) into multidimensional array, by which algorithm gets improved performance compare to the conventional methods. In nested array, power spectrum is measured by using multiple signal classification (MUSIC) and performance index is evaluated using Monte Carlo simulation.

In this paper, cell searching and direction-of-arrival (DoA) estimation methods are proposed for mobile relay stations with a uniform linear arrays in OFDM-based cellular systems. The proposed methods can improve the performance of cell searching and DoA estimation, even when there exist symbol timing offsets among the signals received from adjacent base stations and Doppler frequency shifts caused by the movement of the mobile relay station. The performances and computational complexities of the proposed cell searching and DoA estimation methods are evaluated by computer simulation under a mobile WiMAX environment.

In this paper, an effective joint direction-of-arrival (DOA) and propagation delay estimation method is proposed for multipath signals impinging the uniform linear antenna array. In this presented method, the real and imaginary parts of the ith eigenvalue of a matrix are one-to-one related to the DOA and propagation delay of the ith path's signal. Thus, the paring of the estimated DOAs and delays is automatically determined. Compared with the previous works, the proposed method can provide better performance, such as it can avoid the complex joint diagonal processing, which reduces the computation complexity. The performances of relevant algorithms are compared via simulations, and the effectiveness of the proposed method is verified.

Most of the single-snapshot direction-of-arrival (DOA) estimation algorithms fail to estimate off-grid sources due to finite discretized sampling grid points, such as conventional beamforming and compressive sensing based methods. Meanwhile, existing effective off-grid DOA estimation algorithms based on Fourier coefficients interpolation are only applicable to uniform linear arrays (ULAs). In order to overcome these two shortcomings, we propose an off-grid DOA estimation technique for arbitrary-spaced linear arrays using single snapshot in this work. The initial DOA estimates are obtained using conventional beamforming in the first step. Subsequently, a fine estimation is deployed to correct the angle bias of single off-grid source based on a closed-form formula of the angle offset. In the case of multiple sources, an estimate-subtract strategy is adopted, where remaining sources other than the estimated one are removed from the received data utilizing the estimated information and each source is sequentially corrected in one iteration based on the method of single source case. Simulation results demonstrate the effectiveness and efficiency of the proposed method for off-grid DOA estimation with arbitrary-spaced linear arrays.

Direction of arrival (DOA) estimation performance severely degrades for the reasons that the perturbation from their assumed nominal complex gain values in practical sensor system. In this paper, to mitigate the effects of the uncertainty perturbation, a two-step method for joint DOAs and sensor errors estimation is proposed. In the first step, convex optimization and l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm penalties are used to conduct a second-order cone framework to estimate DOAs roughly under the assumption that all sensor gains are identity. Second, we calibrate the perturbed gains with the DOAs estimated by the first step. The iterative process of the DOAs and gains estimation is taken until convergence. Furthermore, a refinement procedure is addressed to alleviate the effects of the grid limitations to spatial locations. In the present method, no auxiliary calibrated sensors or co-operation signal source is required. And the two-step method can handle both uncorrelated signals and coherent signals, it is also a high resolution method and robust in the condition of low signal-to-noise ratio (SNR). Several aspects are taken into consideration, and numerical experimental results are given to show the advantages of the new method for the DOA estimation.

For the traditional uniform linear array (ULA) direction of arrival (DOA) estimation method with a limited array aperture, a non-circular signal off-grid sparse Bayesian DOA estimation method based on nested arrays is proposed. Firstly, the extended matrix of the received data is constructed by taking advantage of the fact that the statistical properties of non-circular signals are not rotationally invariant. Secondly, we use the difference and sum co-arrays for the nested array technique, thus increasing the array aperture and improving the estimation accuracy. Finally, we take the noise as part of the interest signal and iteratively update the grid points using the sparse Bayesian learning (SBL) method to eliminate the modeling errors caused by off-grid gaps. The simulation results show that the proposed algorithm can improve the accuracy of DOA estimation compared with the existing algorithms.

This paper addresses the source association (SA), direction of arrival (DOA), and fading coefficients (FCs) estimation problem in multipath environment. First, we establish a rank reduction property for a multipath signal model with the existence of multiple groups of coherent signals. Subsequently, based on this property, effective algorithms for SA, DOA, and FCs estimation have been developed. The proposed DOA and FCs estimation methods exploit the multipath structure information to achieve improved accuracy. The new DOA estimation methods work well even in the case that the DOAs of the multipath signals associated with different sources are (nearly) overlapped. Meanwhile, the new methods are applicable to arbitrary array geometry while without decreasing the effective array aperture. Then, the stochastic Cramér-Rao bound on DOA and FCs estimation of multipath model (MCRB) exploiting the multipath structure information is derived in closed form. Numerical simulations have been provided to demonstrate the effectiveness of the proposed methods.

In this paper, the synthetic aperture radar tomography (SARTom) vertical distribution estimation problem is treated within the direction of arrival (DOA) estimation framework. Super-resolution parametric DOA estimation methods improve the vertical resolution and mitigate the effect of sidelobes. Nevertheless, these techniques have the main drawback related to the assumption that the scene is composed by a finite number of point-type backscattering sources. On the other hand, the minimum variance distortionless response (MVDR) inspired non-parametric DOA estimation methods are better suited to cope with scenarios characterized by the presence of distributed scatterers. In this work, we propose to decompose the SARTom vertical distribution estimation problem into two paradigms: parametric DOA estimation for point-type scatterers and non-parametric recovery of the spatial spectrum pattern (SSP) of the spatially distributed scattering components. The principal innovative contribution of this study relates to the proposition for fusion of the Prony-inspired parametric and MVDRinspired non-parametric DOA estimation paradigms through the use of the spectral positional invariance property of the point-type targets, which holds with the extended Prony model.

With the dramatic advances in mobile communication technologies, the problem of estimating the direction of arrival (DOA) of signals generated by several sources becomes even more important. High‐resolution estimation methods based on eigenvalue decomposition include the minimum‐norm method and the MUSIC method. Since the peak of the angular spectrum must be scanned over all directions, the computational effort is enormous. In this paper, a DOA estimation method using a database is proposed. In this method, a database is constructed in which the eigenvector corresponding to the minimum eigenvalue is the key and the DOA is the associated value. The DOA is estimated by seeking in the database the data with the key closest to the eigenvector obtained from the observation signals. In the proposed method, DOA estimation can be achieved by only a regional search of the database, and hence the process is extremely fast. Finally, the effectiveness of the proposed method is demonstrated by simulation. © 2006 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 90(3): 48–56, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.20251

Publication in the conference proceedings of EUSIPCO, Marrakech, Morocco, 2013

This paper mainly introduces a novel estimation method for direction of arrival (DOA) estimation. Recently, direction-of-arrival (DOA) estimation has been widely employed in wireless communication networks, electromagnetic (EM) environment management, and radar tracking. However, traditional DOA estimation algorithms and systems are based on multiple digital receivers with expensive hardware. To overcome this challenge, we propose a novel DOA estimation method based on a space-time coding method and an easy-configurable antenna array. With this programmable L-shape antenna array and space-time coding method, a two-dimensional DOA estimation is realized.

A structured least square (LS) method for simultaneous estimation of the mutual coupling matrix (MCM) and directions of arrival (DOAs) of signal sources for uniform linear array (ULA) and uniform circular array (UCA) is presented. The DOAs and MCM can be simultaneously estimated by the proposed method when observations of at least two different DOAs are available. This method significantly simplifies the procedure of mutual coupling compensation and it can also be applied for the calibration of ULA and UCA. Simulation results demonstrate the efficiency and accuracy of the proposed method.

Direction of arrival (DOA) estimation for multi-channel speech enhancement is a challenging problem. In this context, this paper proposes a new method for joint DOA estimation using a low complexity convolutional neural network (CNN) architecture. The spherical harmonic (SH) coefficients of the received speech signal are obtained from the spherical harmonics decomposition (SHD). The magnitude and phase features are extracted from these SH coefficients and combined as a single feature for training the CNN. A single CNN model is trained using these combined features in contrast to two CNN models used in earlier work. Both azimuth and elevation are then obtained for estimation of DOA from this single CNN. Extensive simulations are also conducted for the performance evaluation of the proposed low complexity CNN model. It is observed that the proposed CNN model provides robust DOA estimates at the various signal to noise ratios (SNR) and reverberation times with reduced computational complexity. Performance evaluated in terms of the gross error (GE) and run-time complexity also provides interesting results motivating the use of the proposed model in practical applications.

In the last few years, the demand of robust beamforming techniques has been increased significantly to serve numerous purposes. However, beamforming under low Signal to Noise Ratio (SNR) condition has always been a challenging task, which becomes more tedious in case of multiple sources. On the other hand, there are numerous factors such as steering vector error or similar estimation error problems that confine the efficiency of major beamforming techniques. Classical methods employ Eigen-Value Decomposition (EVD) and Singular Value Decomposition (SVD) over covariance matrix of the received signal; however their efficiency is limited under low SNR and multi-source condition, and perform inferiorly in case of steering vector errors. Such approaches are limited to achieve optimal Direction-of-Arrival (DOA) estimation that degrades the overall performance of the beamformer. Considering all these limitations, in this research, the focus is made on designing a robust DOA estimation model, followed by a novel beamformer for 8-element Uniform Linear Array (ULA). The simulation work consists two parts DOA estimation and beamforming. Unlike classical MUSIC and ESPRIT algorithms popular for DOA estimation our proposed model exploits the propagation method based subspace method where at first covariance matrix is obtained for the received signal, which is followed by normalization and Cross-Correlation matrix estimation. The obtained correlation matrix is processed for Generalized Eigen-Value Decomposition (GEVD) that efficiently obtained (orthogonal) subspace and SOI separation to yield DOA information. This approach enables more efficient DOA estimation even under low-SNR and steering vector error conditions. After obtaining DOA information, a novel adaptive beamformer is designed by using MVDR and FROST in parallel. DOA information from proposed model is given as input to the adaptive MVDR FROST beamforming model. Exploiting dynamic Signal-to-Interference ratio (SINR) by each beamformer our proposed method introduces an adaptation model that intends to achieve higher SINR value. MATLAB 2018a based simulation confirmed the robustness of the proposed model in terms of DOA estimation and beamforming results.

Recently developed super nested array families have drawn much attention owing to their merits on keeping the benefits of the standard nested arrays while further mitigating coupling in dense subarray portions. In this communication, a new mutual coupling model for nested arrays is constructed. Analyzing the structure of the newly formed mutual coupling matrix, a transformation of the distorted steering vector to separate angular information from the mutual coupling coefficients is revealed. By this property, direction of arrival (DOA) estimates can be determined via a grid search for the minimum of a determinant function of DOA, which is induced by the rank reduction property. We also extend the robust DOA estimation method to accommodate the unknown mutual coupling and gain-phase mismatches in the nested array. Compared with the schemes of super nested array families on reducing the mutual coupling effects, the solutions presented in this paper has two advantages: (a) It is applicable to the standard nested arrays without rearranging the configuration to increase the inter-element spacing, alleviating the cross talk in dense uniform linear arrays (ULAs) as well as gain-phase errors in sparse ULA parts; (b) Perturbations in nested arrays are estimated in colored noise, which is significant but rarely discussed before. Simulations results corroborate the superiority of the proposed methods using fourth-order cumulants.