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The objective of this paper is to present the performance of Direction of Arrival(DoA) estimation algorithms for underwater sound source localization using an acoustic Vector Sensor Array (VSA) that is developed by the National Institute of Ocean Technology, Chennai. Algorithms such as conventional beam forming, Multiple Signal Classification (MUSIC) with Eigen value decomposition, and MUSIC with Singular Value Decomposition (SVD) are used for estimation of DoA and performance study. An experiment has been conducted with the VSA at the Acoustic Test Facility of NIOT with the source transmission of 1 kHz to 5 kHz for different azimuth angles. The estimation of DoA using the above three algorithms and the comparison of the results on resolution and accuracy have been studied in detail in terms of the number of vector elements. Results reveal that the MUSIC method gives results with higher accuracy and resolution than the conventional method. The maximum deviation from the true angle in the conventional method is 4°; in MUSIC, it is 2°, whereas in MUSIC with SVD, it is 1°. While the standard MUSIC algorithm involves computing the eigenvectors of the covariance matrix, which can be computationally expensive, MUSIC with SVD provides a more efficient way to achieve better results. SVD enables straightforward computation of the signal subspace, making it more practical for real-time applications like coastal surveillance. Further to the laboratory experiment, the vector sensor system has been deployed in an open sea environment near the harbor and a known source experiment is carried out. The DoA estimated using MUSIC with SVD for the field data reveals that the results are in good comparison with the measured azimuth and elevation positions. The deviations in the field results are due to dynamic conditions of the ocean,and more sea trials need to be carried out for further study. [ABSTRACT FROM AUTHOR]

In this paper a comparative study, restricted to one-dimensional stationary case, between several Direction of Arrival (DOA) estimation algorithms of narrowband signals is presented. The informative signals are corrupted by an Additive White Gaussian Noise (AWGN), to show the performance of each method by applying directly the algorithms without pre-processing techniques such as forward-backward averaging or spatial smoothing. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is a fundamental problem in underwater acoustic vector sensor array signal processing. Because of the advantages of deep learning technology, this paper proposes two categories of data-driven DOA estimation methods for underwater acoustic vector sensor array, which transform the DOA estimation problem into a neural network classification problem. Specifically, one is the DOA estimation method of convolutional neural network based on teacher-student noise reduction (TS-CNN), which considers the covariance matrix as the training data set; the other is a DOA estimation method based on long-short term memory network and attention mechanism (LSTM-ATT), which applies the timedomain signal as the training data set. The experimental simulation results show that: 1) when the number of array elements is small, the accuracy of the DOA estimation method based on TS-CNN is equivalent to that of traditional methods, and it can effectively suppress the influence of noise when the signal-to-noise ratio (SNR) is low; 2) the accuracy of DOA estimation method based on LSTM-ATT is much higher than that of traditional Multiple Signal Classification method, especially in the case of low SNR, which also proves the importance of temporal characteristics for DOA estimation in a real environment. [ABSTRACT FROM AUTHOR]

Herein, we estimate the direction of arrival (DOA) of non‐Gaussian signals for nested arrays (NAs) by implementing the fourth‐order difference co‐array (FODC) and successive methods. In particular, considering the property of the fourth‐order cumulant (FOC), we first construct the FODC of the NA, which can obtain O(N4) virtual elements using N physical sensors, whereas conventional FOC methods can only obtain O(N2) virtual elements. In addition, the closed‐form expression of FODC is presented to verify the enhanced degrees of freedom (DOFs). Subsequently, we exploit the vectorized FOC (VFOC) matrix to match the FODC of the NA. Notably, the VFOC matrix is a single snapshot vector, and the initial DOA estimates can be obtained via the discrete Fourier transform method under the underdetermined correlation matrix condition, which utilizes the complete DOFs of the FODC. Finally, fine estimates are obtained through the spatial smoothing‐Capon method with partial spectrum searching. Numerical simulation verifies the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector‐sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS‐based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable. [ABSTRACT FROM AUTHOR]

In this paper, the authors propose an effective classification and localization algorithm of mixed far-feld and near-field sources using a uniform circular array under an unknown mutual coupling. In practice, the assumption of an ideal receiving sensor array is rarely satisfied. The effects of unknown mutual coupling would degrade the performance of most high resolution algorithms. Firstly, according to rank reduction type method, the direction of arrival of far-field sources is estimated directly without mutual coupling elimination. Then, these estimates are adopted to reconstruct the mutual coupling matrix. Finally, both direction and range parameters of near-field sources are obtained through MUSIC search after mutual coupling effects and far-field components elimination. The proposed algorithm only requires the second order cumulant and any three dimensional spectrum search is circumvented. Some simulation results would prove that the proposed algorithm can reduce more than eighty percent estimating error of mixed sources localization compared to those algorithms without mutual coupling compensation. [ABSTRACT FROM AUTHOR]

Abstract This study developed an 8-channel microphone array to track the location of a sound source in real time. In addition, a system that implements the GCC–PHAT function as the main algorithm was developed to measure the direction of arrival (DoA) in four spaces with different space volumes and reverberation times (RTs). The developed system exhibited usability in indoor environments with high background noises as it extracted DoA with errors lower than 3° when the signal-to-noise ratio (SNR) was higher than 5 dB for white noise. In addition, as is generally known, the accuracy of a localization system decreases as the space volume and RT increase. However, a higher DoA accuracy was observed in large spaces such as concert halls, in which the RT is high but early reflective surfaces are located far away, compared with that in spaces such as lecture rooms, in which the RT is relatively low and early reflections from nearby furniture and walls. Speech intelligibility was the highest when strong early reflections within 20 dB (compared with direct sound) were formed in the error range of ±3.2°. Furthermore, it was verified that the system was most effective when the frame length, which is a real-time sound source analysis parameter, was 5.8 ms to extract a small amount of operation and the amount of DoA information within the same time period through the evaluation results in the four spaces. Highlights • Investigated impacts of building environment on a DoA estimation. • Developed 8-channel linear microphone array and GCC-PHAT-based system. • Evaluated DOA characteristics according to space volume and reverberation. • Optimal frame length for the real-time system was selected as 5.3 ms • Strong early reflections within 20 dB have large impacts on speech intelligibility. [ABSTRACT FROM AUTHOR]

The resolution of a Direction of Arrival (DOA) es- timation algorithm is determined based on its capability to re- solve two closely spaced signals. In this paper, authors present and discuss the minimum number of array elements needed for the resolution of nearby sources in several DOA estima- tion methods. In the real world, the informative signals are corrupted by Additive White Gaussian Noise (AWGN). Thus, a higher signal-to-noise ratio (SNR) offers a better resolution. Therefore, we show the performance of each method by ap- plying the algorithms in different noise level environments. [ABSTRACT FROM AUTHOR]

The utilisation of smart antenna (SA) techniques in future wireless and cellular networks is expected to have an impact on the efficient use of the spectrum and the optimization of service quality. This is because SAs can enhance the maximization of output power of the signal in desired directions amongst a whole lot of functions. Despite these benefits of SAs, long range communications still face unsolved challenges such as signal fading. Therefore, this paper focuses on enhancing the recursive least squares (RLS) in SA design for long range communication networks. The conventional RLS algorithm does not need any matrix inversion computations because the inverse correlation matrix is determined directly. Therefore, the RLS saves computational power. Hence, we have enhanced the RLS algorithm by introducing a constant m to the gain factor in order to yield an improved gain vector. Results from our simulations show that the enhanced RLS reduces mean square error (MSE), smoothens filter output and improves signal-to-noise ratio (SNR). These benefits further result in the antenna’s gain improvement leading to an increased range and directivity of the smart antenna over a long range communication networks. [ABSTRACT FROM AUTHOR]

Adaptive antennas and antenna array processing are much significant issues for improving the performance of wireless communication systems. One of the most important applications of adaptive antenna systems is the Direction of Arrival (DOA) estimation. Recently, compressive sensing algorithms, including convex relaxation methods and greedy algorithms, have been recognized as a type of novel DOA estimation methods. The Orthogonal Matching Pursuit (OMP) is an example of compressive sensing methods. Using the OMP method for DOA estimation has many advantages in comparison to other algorithms. In spite of these advantages, the DOA estimation by OMP algorithm has a substantial challenge. The OMP algorithm cannot distinguish between two adjacent signal sources. In the DOA estimation by OMP algorithm, when there are two adjacent sources, the mutual coherent condition of the compressive sensing methods is violated. The situation gets worse when there are two sources from two adjacent DOAs. In this situation, the beam former has a single peak. In this paper, we propose the Focused Orthogonal Matching Pursuit (FOMP) algorithm for estimation of DOA. The FOMP algorithm is an improved version of the OMP algorithm. It can detect two signal sources exactly when they are very close and beam former has a single peak corresponding to a direction between right directions. Simulation results demonstrate the advantages of the proposed scheme. It can be observed the FOMP algorithm could detect very close signal sources with a negligible error. [ABSTRACT FROM AUTHOR]

Numerous methods exist for estimating the direction of arrival (DOA) of distributed sources in the presence of the Gaussian noises. However, they are inefficient when impulsive signals and noises are present. A novel empirical characteristic function (ECF)-based ESPRIT approach for localizing coherently and incoherently distributed sources is proposed in this study. This method makes use of the interesting properties of characteristic functions like simplicity, information preservation, analytic form, and high precision to build a technique that really is accurate and robust against impulsive signals and noises. The array characteristic function (CF) is modeled using the α -stable distribution to represent the impulsive nature of the signals and sources. This function generates a covariance matrix that can be utilized directly in ESPRIT algorithm. Due to the interesting properties of the ECF, an accurate algorithm is developed that is highly effective in suppressing the effect of impulsive noises, especially when the signal and noise have extremely heavy tails, the signal-to-noise ratio is low, and only a small number of snapshots are available. The results of Monte-Carlo simulation indicate that the suggested method beats existing methods in terms of resolution probability and estimation error. Therefore, this strategy is strongly recommended for localizing coherently and incoherently distributed sources in environments with strong impulses. [ABSTRACT FROM AUTHOR]

When studying the radiation coming from far field sources using an array of sensors, besides the internal thermal noise, the received wave field is always perturbed by an external noise field, which can be temporally and spatially coherent to some degree, temporally incoherent and spatially coherent, spatially incoherent and temporally correlated or finally, the incoherence in both domains. Thus treating the received data needs to consider the nature of perturbing field in order to make accurate measurements such as powers of punctual sources, theirs locations and the types of waveforms which can be deterministic or random. In this paper, we study the type of temporally white and spatially coherent noise field; we propose a new spatial coherence function using Lorentz function. After briefly describing some existing models, we numerically study the effect of spatial coherence length on resolving the angular locations of closely radiating sources using spectral techniques which are divided into beam forming and subspace based methods, this study is made comparatively to temporally and spatially white noise with the same power as the proposed one in order to make a precise comparisons. [ABSTRACT FROM AUTHOR]

The Internet of Things environment supports the device-to-device communication. These devices respond to the events generated to serve various applications. The processing of these applications need reliable data dissemination to achieve accuracy in data distribution. In such cases, localization of events occurring in the environment becomes most crucial. Moreover, in heterogeneous IoT scenario, event localization may help in real time service delivery. This paper proposes a Direction of Arrival based event localization method for IoT environment. The proposed work uses auto-regression modeling to estimate the location of events. The efficacy of the proposed algorithm is evaluated in IoT based environment. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology 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 Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology 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.)

Source localization problem consists of an ensemble of techniques that are used to obtain spatial information of present radiation in given medium of propagation, with a constraint of the antenna geometry and the characteristics of radiating sources. This condition gives multitude of cases to study, hence several methods were proposed in the literature. In this paper, a new algorithm for estimating the Direction of Arrival (DoA) of narrowband and far field punctual sources is introduced. By exploiting the spectrum of covariance matrix of received data, the Lorentzian function on spectral matrix to filter the eigenvalues is applied. This filtering process eliminates the eigenvalues belonging to signal subspace. Parameters of Lorentz function are adjusted using first and second statistics of eigenvalues. The algorithm requires the knowledge of minimum eigenvalue and is performing when the dimension of antenna is relatively large which is confirmed by several Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of Beijing Institute of Technology is the property of Beijing University of Technology 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.)

The direction of arrival of the incoming signals to an antenna array is one of the properties which can be obtained through the processing of the received data. This property is an important key in the development of adaptive antennas, enabling to discover the number of impinging signals to an antenna and its directions. This paper describes the most known algorithms that determine the directions of arrival using a two dimensional antenna array, analyzing its performance. Properties such as the computational efficiency and estimation accuracy using several scenarios of noise are presented. [ABSTRACT FROM AUTHOR]

The direction of arrival of the incoming signals to an antenna array is one of the properties which can be obtained through the processing of the received data. This property is an important key in the development of adaptive antennas, enabling to discover the number of impinging signals to an antenna and its directions. This paper describes the most known algorithms that determine the directions of arrival using a two dimensional antenna array, analyzing its performance. Properties such as the computational efficiency and estimation accuracy using several scenarios of noise are presented. [ABSTRACT FROM AUTHOR]

Based on uniform circular arrays (UCA), a direction of arrival (DOA) estimation algorithm of coherent signals (DD-EVD) with single snapshot data was proposed. The algorithm used mode excitation to transform the manifold matrix of UCA into uniform linear array. Then, a Toeplitz matrix which was reconstructed with the data vectors of beam space was decomposed in accordance with array manifold. It was proved that the rank of this Toeplitz matrix was regained and determined merely by the number of incident sources. Accurate signal subspace and noise subspace can be acquired by performing eigenvalue decomposition of the matrix. Combined with the subspace kind algorithms, the DOA of coherent signals was estimated. Because of using single snapshot data to reconstruct matrix, the algorithm does not need correlation calculation and cumulation of snapshots. In view of this, the algorithm is intended to estimate the DOA of non-stationary signals and the complexity of the algorithm is reduced. Finally, the simulation verifies the effectiveness. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of joint time of arrival (TOA) and direction of arrival (DOA) estimation in impulse radio ultra-wideband systems with a two-antenna receiver and links the joint estimation of TOA and DOA to the sparse representation framework. Exploiting this link, an orthogonal matching pursuit algorithm is used for TOA estimation in the two antennas, and then the DOA parameters are estimated via the difference in the TOAs between the two antennas. The proposed algorithm can work well with a single measurement vector and can pair TOA and DOA parameters. Furthermore, it has better parameter-estimation performance than traditional propagator methods, such as, estimation of signal parameters via rotational invariance techniques algorithms matrix pencil algorithms, and other new jointestimation schemes, with one single snapshot. The simulation results verify the usefulness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper a comparative study, restricted to one-dimensional stationary case, between several Direction of Arrival (DOA) estimation algorithms of narrowband signals is presented. The informative signals are corrupted by an Additive White Gaussian Noise (AWGN), to show the performance of each method by applying directly the algorithms without pre-processing techniques such as forward-backward averaging or spatial smoothing. [ABSTRACT FROM AUTHOR]

Direction of Arrival (DOA) estimation by Maximum Likelihood (ML) method has been popular among researchers due to its effective performance. However, its practical applications are limited by its computational complexity in non-linear multidimensional solution search which requires optimization. In order to make accurate non-linear ML-DOA estimation, simultaneously reducing the computational complexity involved in the process evolutionary algorithms are employed in this manuscript. In this manuscript, a recently reported nature-inspired optimization algorithm termed as 'sailfish optimizer' has been applied for non-linear multidimensional DOA estimation. The sailfish optimization is based on the swarming behavior of a group of sailfish to catch the prey which are sardines. The algorithm has been designed with both random and chaotic sequence-based population initialization. Simulation studies reveal that the chaotic sequence-based algorithm has lower computational time and accurate DOA estimation compared to its random counterpart. A parallel implementation of the chaotic version of the algorithm has been carried out to reduce the computational aspect of the algorithm. Simulation studies reveal the parallel version has almost 50% lower run time than that of the sequential one for 2048 snapshots implementations. By varying the number of sources, sensors, and snapshots experimentation has been carried out which reveals accurate DOA estimation (more than 95% angle accuracy in most of the cases) with reduced computational time (due to parallel implementation) achieved with the proposed algorithm. The comparative analysis of the proposed algorithm has been carried out with that achieved by benchmark MUSIC (Multiple Signal Classification) algorithm and nature inspired algorithms like : Advanced Particle Swarm Optimization, Real Coded Genetic Algorithm, Differential Evolution, CLONAL Selection, and Artificial Bee Colony algorithm. Kruskal–Wallis non-parametric test is performed under various noise conditions to validate the superior performance of the proposed approach over the comparative algorithms. [ABSTRACT FROM AUTHOR]

In order to estimate two-dimensional (2-D) direction-of-arrival (DOA) of coherent signal sources fast in complex electromagnetic interference environments, a novel two- dimensional ESPRIT method called single snapshot ESPRIT algorithm (SS-ESPRIT) is proposed. The proposed algorithm reconstructs four equivalent covariance matrices using only one snapshot data from arrays. The four equivalent covariance matrices compose an extended equivalent covariance matrix. De-correlation and 2-D DOA estimation could be accomplished via the eigen-decomposition of the matrix. The strategy of in-phased data accumulation is also proposed to improve the performance of SS-ESPRIT algorithm. Simulation results show that SS-ESPRIT algorithm could realize fast estimation of 2-D DOA without causing the performance degradation. The estimation performance of new algorithm using single snapshot is better than that of direction-of-arrival matrix (DOAM) algorithm using 50 snapshots and is close to the performance of DOAM algorithm using 100 snapshots. The estimation performance of the algorithm using 8 in-phased data is better than that of DOAM algorithm using 200 snapshots. It could be concluded that the algorithm proposed in this research is suitable for application in short-time sampled data and real-time estimation. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays all the research about the estimation of MIMO (multiple input and multiple output) radar''s DOA (direction of arrival) is based on traditional algorithm. Therefore, it cannot estimate the cyclostationary signal''s DOA and this kind of signals are widely used in the fields of signal processing. Hence, this paper proposes a new estimation algorithm of MIMO radar''s DOA which based on cyclic statistics; Uniform circular array can do a 360-degree non-fuzzy DOA estimation and also provide azimuth and angle of pitch information. Therefore, the theoretical derivation of the new algorithm in a uniform circular array can estimate pitch angle and azimuth at the same time in the non-stationary signal environment. [Copyright &y& Elsevier]

To estimate direction of arrival for the coherent signals, a virtual array method for direction of arrival estimation based on unitary transformation is proposed. In this approach, the covariance matrix, which is obtained from virtual array, is reconstructed by utilizing a unitary transformation matrix. The simulation results verify that the proposed method has high performance of de-correlation and estimation. The proposed method can de-correlate the coherent sources without a lot of snapshots and a reduction in array aperture. All of matrix computation are in real-valued space, thus, the computation complexity is very low. [ABSTRACT FROM AUTHOR]

A new method is presented for reducing the computational load and removing mismatch between the azimuth and elevation angle existing in estimation of subspace rotational in variance technology(ESPRIT) algorithm. Instead of eigen-decomposition, the propagator method is applied to generate rotational invariance matrix, and the elevation angle estimation is derived from the estimated azimuth angle. Thus, the computational load can be decreased greatly, and parameters paired automatically. In addition, a novel three-dimensional array is designed to accurately estimate the source signal from any direction. Simulation results showed that the proposed method is characterized by fast computation speed and high precision. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd 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.)