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Abstract In propagator based methods for Direction of Arrival (DoA) finding, conventional approaches for eliminating the noise impact are based on signal enhancement or noise power estimation. In this paper, a new method is introduced from another point of view. When the noise is an additive spatially and temporally white Gaussian noise, theoretically, it only impacts the diagonal elements of the data covariance matrix. Firstly, the proposed method utilizes the principle of Linear Prediction (LP) to rebuild the denoised diagonal elements of the data covariance matrix. Then, the Orthogonal Propagator Method (OPM) is directly applied for DoA estimation. Compared with the conventional OPM-based methods, the proposed method is more robust to the noise, especially in low Signal to Noise Ratio (SNR) scenarios. Simulation examples are provided to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Direction finding (DF) using the uniform circular array composed of horizontal log-periodic dipole antennas (UCA-LPDA) is studied in this paper. One advantage of the UCA-LPDA is that it has a reasonable effective radius for broadband DF. Compared with the previous works which usually focused on isotropic antennas, the UCA-LPDA has higher gain and higher angular resolution for the direction of arrival estimation. The spatial response of the UCA-LPDA and the direction of arrival (DOA) estimation algorithms are discussed. The proposed polarized MUSIC (Pol-MUSIC) method can obtain the DOA of signals with any unknown polarizations while no search of the polarizations is required. Based on the theoretical analysis, the actual signal DF experiment which uses a UCA-LPDA with 24 elements was carried out. The DF experiments results demonstrate the effectiveness and accuracy of using UCA-LPDA with Pol-MUSIC method. [ABSTRACT FROM AUTHOR]

To address the issue of serious decline in performance of the array signal processing caused by the towed array shape distortion during maneuvering, this paper presents a new method of subarray beam-space adaptive beamforming combined with an array shape estimation method based on non-acoustic sensor. Firstly, the array shape through the approximate circular arc structure of the array segment between the adjacent sensor during maneuvering is preliminary calculated. Next, the final estimated array shape through a smooth processing method on the entire array shape by means of a calibration method using spline interpolation technique is achieved. This method is able to estimate the array shape in real time. The array steering vector based on the real-time estimation of the array shape is updated, using a subarray beam-space adaptive beamforming (SBABF) to reduce the demand of the number of the snapshots. And the beam-space covariance matrix converges fast within a few snapshots. The SBABF method combined with array shape estimation (AE-SBABF) was verified by simulation data and sea trial data processing results. During maneuvering, the AE-SBABF can not only improve the array processing gain of the target effectively, but also solve the left/right ambiguity problem well. [ABSTRACT FROM AUTHOR]

In this paper, a novel off-grid direction-of-arrival (DOA) estimation algorithm is proposed based on steering vector approximation. The conventional multiple signal classification (MUSIC) technique estimates the DOA with a uniformly discretized grid by assuming that the true DOAs lie on the grids. In practice, this assumption is rarely satisfied. In order to improve the estimation accuracy, a dense sampling grid is required for the MUSIC technique. Besides the higher computational complexity, it is still not guaranteed that the dense sampling grids will cover the true DOAs. In order to address the off-grid problem with a lower computational complexity, we use the Taylor series expansion to approximate the steering vectors. We first construct a two-dimensional spectrum searching method to simultaneously estimate the nearest on-grid DOA and the corresponding off-grid error. To further improve the estimation accuracy, we then solve an optimization problem to obtain the accurate DOA estimation, which is formulated by the fact that the signal and noise subspaces are orthogonal to each other. Simulation results demonstrate the effectiveness of the proposed off-grid DOA estimation algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose a convolutional recurrent neural network for joint sound event localization and detection (SELD) of multiple overlapping sound events in three-dimensional (3-D) space. The proposed network takes a sequence of consecutive spectrogram time frames as input and maps it to two outputs in parallel. As the first output, the sound event detection (SED) is performed as a multi-label classification task on each time frame producing temporal activity for all the sound event classes. As the second output, localization is performed by estimating the 3-D Cartesian coordinates of the direction-of-arrival (DOA) for each sound event class using multi-output regression. The proposed method is able to associate multiple DOAs with respective sound event labels and further track this association with respect to time. The proposed method uses separately the phase and magnitude component of the spectrogram calculated on each audio channel as the feature, thereby avoiding any method- and array-specific feature extraction. The method is evaluated on five Ambisonic and two circular array format datasets with different overlapping sound events in anechoic, reverberant, and real-life scenarios. The proposed method is compared with two SED, three DOA estimation, and one SELD baselines. The results show that the proposed method is generic and applicable to any array structures, robust to unseen DOA values, reverberation, and low SNR scenarios. The proposed method achieved a consistently higher recall of the estimated number of DOAs across datasets in comparison to the best baseline. Additionally, this recall was observed to be significantly better than the best baseline method for a higher number of overlapping sound events. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation for speech sources is an important task in audio signal processing. This task becomes a challenge in reverberant environments, which are typical to real scenarios. Several methods of DOA estimation for speech sources have been developed recently, in an attempt to overcome the effect of reverberation. One effective approach aims to identify time-frequency bins in the short time Fourier transform domain that are dominated by the direct sound. This approach was shown to be particularly adequate for spherical arrays, with processing in the spherical harmonics domain. The direct-path dominance (DPD) test, and a method which is based on the directivity of the sound field are recent examples. While these methods seem to perform well, high reverberation conditions may degrade their performance. In this paper, the structure of the spatial correlation matrix is comprehensively studied, showing that under some well-defined conditions, the DOA of the direct sound can be correctly extracted from its dominant eigenvector, even when contaminated by reflections. This new insight leads to the development of a new test, performing an enhanced decomposition of the direct sound (EDS), denoted the DPD-EDS test. The proposed test is compared to previous DPD tests, and to other recently proposed reverberation-robust methods, using computer simulations and an experimental study, demonstrating its potential advantage. The studies include multiple speakers in highly reverberant environments, therefore representing challenging real-life acoustics scenes. [ABSTRACT FROM AUTHOR]

This paper discovers rules-of-thumb on how the estimation precision for an incident source's azimuth-polar direction-of-arrival (ϕ ,θ) depends on the number (L) of identical isotropic sensors spaced uniformly on an open sphere of radius R. This estimation's corresponding Cramér–Rao bounds (CRB) are found to follow these elegantly simple approximations, useful for array design: (i) For the azimuth arrival angle: 2 π (R / λ) (σ s / σ n) 2 L M CRB (ϕ) sin (θ) ≈ (L e 1 / 14 ) − 1 + 3 → L → ∞ 3 , ∀ (ϕ , θ) ; and (ii) for the polar arrival angle: 2 π (R / λ) (σ s / σ n) 2 L M CRB (θ) ≈ 3 − (L e 6 / 7 ) − 1 → L → ∞ 3 , ∀ (ϕ , θ). Here, M denotes the number of snapshots, λ refers to the incident signal's wavelength, and ( σ s / σ n ) 2 symbolizes the signal-to-noise power ratio. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The sparse signal recovery-based direction of arrival (DOA) estimation has received a great deal of attention over the past decade. From the sparse representation point of view, ℓ0-norm is the best choice to evaluate the sparsity of a vector. However, solving an ℓ0-norm minimization problem is non-deterministic polynomial hard (NP-hard). Thus, The common idea for many sparse DOA estimation methods is to use the ℓ1-norm as the sparsity metric. However, its sparse solution may not coincide with the solution resulting from the ℓ0-norm thus deteriorating the DOA estimation performance. In this paper, we propose a new sparse method based on ℓp (0

A new algorithm to estimate the direction of arrival (DOA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors is presented by exploiting the canonical polyadic decomposition (CPD) of tensors. In addition to spatial and temporal diversities, further information from the polarization domain is considered and used in this paper. Estimation errors of these parameters are evaluated by the Cramér-Rao lower bound (CRB) benchmark, in the presence of additive white Gaussian noise (AWGN). The superiority of the proposed algorithm is shown by comparing with the derivative algorithms of MUSIC and ESPRIT. In the proposed algorithm, the parameters can be estimated by virtue of the diversities of the spatial and polarization belonging to the factor matrices, rather than the conventional subspace which is the foundation of MUSIC and ESPRIT. Additionally, the classical CPD algorithm based on Alternating Least Squares (ALS) is introduced to verify the efficacy of the proposed CPD algorithm. Results demonstrate that when the number of snapshots is greater than 50, the proposed algorithm requires a smaller number of snapshots to achieve a high level of performance, compared against the subspace-based algorithms and the ALS-based algorithm. Furthermore, in the matter of the array with a small number of sensors, the discovered advantage concerning the Root Mean Square Error (RMSE) in estimating the DOA and the polarization state of the signal is noteworthy. [ABSTRACT FROM AUTHOR]

In this paper a novel robust beamforming method is devised to receive multipath signals effectively. The new algorithm constructs a transformation matrix derived through high-order angle constraint to suppress the interferences with the directions of arrival (DOA) of interference signals. Using the transformed data, the composite steering vector of the multipath signals is estimated as the principal eigenvector of the signal subspace, and then is utilized in minimum variance distortionless response (MVDR) beamforming to compute the optimal weight vector. The new algorithm is improved in robustness to DOA error by forming wide nulls in incident directions of the interferences, and keeps effective in the presence of coherent interferences. Simulations analyses are provided to illustrate the robustness and effectiveness of the new beamformer. [ABSTRACT FROM AUTHOR]

Highlights • A time-space unionized signal reception model is established for the array receiver architecture adopting the technique of sub-Nyquist sampling. • Two joint direction-of-arrival and frequency estimation algorithms are proposed for the reception model. • Derive the corresponding Cramer-Rao Bound for such a model and analyze the computational complexity of the proposed algorithms as well. Abstract Inspired by the concept of sub-Nyquist sampling, in this paper, a receiver architecture of sensor arrays for jointly estimating the frequency and direction-of-arrival (DOA) of the narrowband far-field signals is presented. In particular, our contribution is threefold. (i) First, we propose a time-space unionized signal reception model for array signals, where sub-Nyquist sampling techniques can be used. (ii) Second, based on the reception model, two algorithms for jointly estimating frequency and DOA are proposed. One is based on a trilinear decomposition by the third-order tensor theory and the other is based on subspace decomposition. (iii) Third, we derive the corresponding Cramér-Rao Bound (CRB) for such a model and analyze the computational complexity of the proposed algorithms as well. Simulation results demonstrate that the estimation performance of our proposed algorithms based on the receiver architecture closely match the CRBs with respect to the noise levels, the branch number and the source number. [ABSTRACT FROM AUTHOR]

The manifold of an array with identical antennas is generally assumed to be insensitive to the polarization of the impinging signal except for a complex scale factor which only affects the signal amplitude. Signal polarization is therefore considered in the literature in the context of diversely polarized arrays, which are specifically designed to be sensitive to polarization. In this paper, we show that the manifold of antenna arrays is generally sensitive to signal polarization even when the antennas have the same polarizations (e.g., identical antennas). We develop two measures which indicate the degree to which an array is sensitive to polarization. We show that direction-of-arrival estimation using an array manifold model which is independent of polarization may be biased due to model mismatch and suffer loss of resolution as the signal polarization varies. Numerical examples are presented to verify the analysis. [ABSTRACT FROM AUTHOR]

Direction of Arrival (DOA) estimation is the important stage in a smart antenna that estimates the direction of impinging signals on an antenna array, and the most high-resolution DOA estimation subspace algorithm is Multiple Signal Classification (MUSIC). MUSIC algorithm is best suited for estimating the non-coherent signals due to its accuracy and a high degree of resolution. However, its performance degrades in presence of multipath propagation that has coherent and closely spaced signals and at low Signal to Noise Ratio. Many researchers proposed modifications to the MUSIC algorithm to improve its performance even under multipath, coherent and closely spaced signal environment. This paper focuses on investigating a proposed preprocessing technique, referred to as CW-ICA MUSIC that improves the performance of the MUSIC algorithm under above scenario even at low SNR. Simulation results confirm that the proposed preprocessing technique based on Dual Tree Complex Wavelet Transform (DTCWT) and Independent Component Analysis (ICA) outperforms the reported modified MUSIC algorithms such as Wavelet-MUSIC, and SS-MUSIC. [ABSTRACT FROM AUTHOR]

Abstract: Multi-beam bathymetry sonar is one of the current important equipments for seafloor bathymetry information detection. This paper extends the phase center cross-correlation method (PCCCT) which is successfully used in a 3- row bathymetric sidescan sonar to multi-beam bathymetry sonar, and a 3-subarray Direction-of-Arrival (DOA) estimation method based on phase difference measurement is proposed. A comparison with the split-beam DOA estimation method which is commonly used in multi-beam bathymetry sonar is employed. The paper analyzes the basic principle of the new method in details, and derives its mathematical model expression. The performance analysis is accomplished in theory. Computer simulation data is processed, and the results show that the 3-subarray DOA estimation method is effective and practical for multi-beam bathymetry sonar. [Copyright &y& Elsevier]

This paper treats the problem of joint estimation of time-delay, Doppler frequency, and spatial (direction-of-arrival or DOA) parameters of several replicas of a known signal in an unknown spatially correlated noise field. Both spatially unstructured and structured data models have been proposed for this problem and corresponding maximum likelihood (ML) estimators have been derived. However, structured models require a high computational complexity and are sensitive to the antenna array response, while unstructured models are unable to achieve good performance in some scenarios. In this paper, it is shown how the extended invariance principle (EXIP) can be applied to obtain estimates with the quality of a spatially structured model, but with much lower complexity than directly utilizing a structured model and with greater robustness to errors in the model of the array response. EXIP improves the quality of the time-delay and Doppler frequency estimates obtained with a spatially unstructured model by introducing DOA estimates which are obtained in a second step through an innovative reparametrization. Simulation results for time-delay and Doppler frequency estimation for Global Positioning System (GPS) signals are presented and confirm that the proposed two-step approach attains the Cramer-Rao lower bound (CRLB) of the spatially structured model. [ABSTRACT FROM PUBLISHER]

This paper summarizes the development of safety-of-life receivers for global navigation satellite systems. Two receiver systems have been successfully fabricated, integrated, and tested by means of field experiments. Implementation issues of these two systems are described in this paper. The third generation of this receiving system is based on a dual-band approach with antennas that are able to separate two frequency bands using two outputs. Measured results show that high isolation between the two bands can be achieved. [ABSTRACT FROM AUTHOR]

Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation. [ABSTRACT FROM AUTHOR]

Highlights • A variation of the FRI-based DOA estimation algorithm under closely-spaced sources, low SNR, and small number of snapshots scenarios is proposed. • The MMV-FRI model is built in the direct data domain, which makes the new method be free of model residual error and compatible with coherent sources. • The full column rank constraint on the mapping matrix in FRIDA is relaxed, which reduces the Bessel approximation error to a negligible level. • For FRI iteration, only a single straightforward initialization is used instead of the multiple random initializations. Abstract Recently, a new DOA estimation algorithm called FRIDA was proposed. It is based on spatial finite rate of innovation (FRI) reconstruction and exhibits some attractive features. In this paper, we propose a variation of FRIDA which is called FRIDA-V (suffix “-V” means variation). FRIDA-V improves FRIDA in three aspects. Firstly, a multiple measurement vectors (MMV) spatial FRI model is built in the direct data domain rather than the covariance domain, which avoids the model residual error in FRIDA and makes the new method capable of handling both incoherent and coherent sources. Secondly, the full column rank constraint on the mapping matrix is relaxed by adopting the pseudo-inverse technique, which makes it possible to reduce the Bessel function approximation error to a negligible level. Thirdly, the multiple random initializations in the iterative calculations are replaced by a single straightforward initialization, which will speed up the convergence to the optimal solution and save the computational resource. Theoretical derivations and numerical simulation results are given to demonstrate the effectiveness of the proposed method. Compared with the representative methods, the new method is gridless and possesses higher performance with closely-spaced sources and under low signal-to-noise ratio (SNR), small number of snapshots scenarios. [ABSTRACT FROM AUTHOR]

In massive multiple-input multiple-output (MIMO) systems, accurate direction-of-arrival (DOA) estimation is critical for the transmitters to conduct downlink precoding. In this paper, we focus on the problem of two-dimensional DOA estimation of incoherently distributed sources in massive MIMO systems using uniform rectangular arrays (URAs). First, with the generalized array manifold, we obtain the beamspace array manifold by performing beamspace transformation on the observed vector of the URA, and establish one beamspace shift invariance structure via appropriate beamforming matrix design and array manifold selection. Second, from the beamspace array manifold, we extract two array manifolds, and derive the other beamspace shift invariance structure. Final, the total least squares approach is used to estimate the nominal azimuth and elevation DOAs. With the DOA estimates, the corresponding angular spreads are given in closed-form solutions. Our approach avoids spectral search and reduces the dimensions in matrix operations, thus, it is more computationally attractive than the existing techniques. Numerical results show that the proposed algorithm achieves performance comparable to that of the conventional estimators in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Abstract A procedure of calibration of a custom 3D acoustic vector sensor (AVS) for the purpose of direction of arrival (DoA) estimation, is presented and validated in the paper. AVS devices working on a p-p principle may be constructed from standard pressure sensors and a signal processing system. However, in order to ensure accurate DoA estimation, each sensor needs to be calibrated. The proposed algorithm divides the calibration process into two stages. First, amplitude calibration is performed in order to compensate for amplitude differences between pairs of microphones situated on each axis. After the pressure and velocity signals are computed from the corrected microphone signals, the second stage is performed in order to correct phase differences between the pressure and velocity signals, which then allows for computing the intensity signals for each axis. In order to validate the calibration method, a reference AVS was constructed from low cost components, namely MEMS microphones and a DSP board. The method was engineered with an assumption that it will be applicable to any AVS working on the same principle. A set of experiments was performed in order to validate the calibration method and to compare the accuracy of the calibrated sensor with a commercial AVS. It was found in the experiments that DoA accuracy of the proposed 3D AVS calibrated with the proposed procedure matches that of a commercial, high cost, factory-calibrated sensor. Therefore, the proposed calibration method fulfills the requirements of accurate DoA estimation, and it is applicable to calibration of custom built, low cost AVS devices, that may be implemented in practical applications for determining the direction of sound sources, such as environmental monitoring, traffic monitoring and public security systems. [ABSTRACT FROM AUTHOR]

Abstract Adaptive antennas and antenna array processing have vital effects on enhancing the performance of wireless communication networks. One of the most significant applications of adaptive antenna systems is the Direction of Arrival (DOA) detection. Several schemes have been proposed in the literature for DOA estimation in two-dimensional space. Among them, Orthogonal Matching Pursuit (OMP) algorithm provides several advantages in comparison to other schemes. The OMP algorithm reduces complexity and improves resolution of detection. Furthermore, in this algorithm, the number of signal emitters is not required to be known. In this paper, we extend the OMP algorithm and propose Three Dimensional OMP (3D-OMP) scheme for DOA estimation in three dimensional space. We provide a redundant dictionary for 3D-OMP scheme by employing azimuth and elevation angles. Simulation results show the high performance of the 3D-OMP algorithm when the signal to noise ratio is higher than − 10 d B. Moreover, the 3D-OMP algorithm has a high efficiency in detecting multiple signal sources, simultaneously. Then, we evaluate the accuracy of the estimated directions by the 3D-OMP algorithm via comparing the variance of estimation error with the Cramer-Rao bound. Nevertheless, DOA estimation by the 3D-OMP algorithm has a substantial challenge where it cannot distinguish between two adjacent signal sources. To resolve this challenge, we propose Three Dimensional Focused Orthogonal Matching Pursuit (3D-FOMP) algorithm. The 3D-FOMP algorithm is an improved version of the 3D-OMP algorithm. It can detect two adjacent signal sources when the beam former has a single peak corresponding to a direction between right directions. In addition, we show the 3D-OMP and 3D-FOMP algorithms have lower complexities in comparison to the 3D-MUSIC and 3D-ESPRIT schemes when Root Mean Square Error (RMSE) > 0. 3 o. This accuracy is persuasive for most applications. Moreover, simulation results show the capability of the 3D-FOMP algorithm to detect adjacent signal sources and it outperforms the 3D-OMP, 3D-MUSIC and 3D-ESPRIT algorithms for all values of signal to noise ratio. [ABSTRACT FROM AUTHOR]

This paper proposes a novel source reconstruction method (SRM) based on the convolutional neural network algorithm. The conventional SRM method usually requires the scattered field data oversampled compared to that of target object grids. To achieve higher accuracy, the conventional SRM numerical system is highly singular. To overcome these difficulties, we model the equivalent source reconstruction process using the machine learning. The equivalent sources of the target are constructed by a convolutional neural networks (ConvNets). It allows us to employ less scattered field samples or radar cross section (RCS) data. And the ill-conditioned numerical system is effectively avoided. Numerical examples are provided to demonstrate the validity and accuracy of the proposed approach. Comparison with the traditional NN is also benchmarked. We further expand the proposed method into the direction of arrival (DOA) estimation to demonstrate the generality of the proposed procedure. [ABSTRACT FROM AUTHOR]

For the purpose of target parameter estimation of the orthogonal frequency-division multiplexing (OFDM) radar, a high-resolution method of joint estimation on range and direction of arrival (DOA) based on OFDM array radar is proposed in this paper. It begins with the design and analysis of an echo model of OFDM array radar. Since there is no coupling between range and angle parameter estimation for a narrow-band signal, a method which exploits the data of one snapshot to estimate the range and angle of the target by means of multiple signal classification (MUSIC) based on virtual two-dimensional spatial smoothing in range and angle dimensions is devised. The proposed method is capable of joint estimating the range and DOA of the target in a high resolution under a single snapshot circumstance. Simulation experiments demonstrate the validity of the proposal. [ABSTRACT FROM AUTHOR]

Groups of coordinated underwater vehicles or sensors are powerful tools for monitoring the ocean. A requirement of many coordinated surveys is to determine a spatial reference between each node in a swarm. This work considers the self-localization of a swarm of independently moving vehicles using acoustic noise from a dominating incoherent source recorded by a single hydrophone onboard each vehicle. This method provides an inexpensive and infrastructure-free spatial reference between vehicles. Movement between the vehicles changes the swarm geometry and a self-localization estimate must be generated from data collected on short time scales. This challenges past self-localization approaches for acoustic arrays. To overcome this challenge, the proposed self-localization algorithm jointly estimates the vehicle geometry and the directionality of the ambient noise field, without prior knowledge of either estimate. To demonstrate this method, experimental results are provided when a boat is the main dominating source. The results demonstrate the ability to both estimate the direction of arrival of the boat and the relative positions of the vehicles in the swarm. The approach in this paper is not limited to moving vessels. Simulations are provided to examine three different factors that affect the proposed solution: inter-vehicle motion, vehicle geometry, and the azimuthal variance of the noise field. [ABSTRACT FROM AUTHOR]

In this paper, direction-of-arrival (DOA) estimation using non-coherent processing for partly calibrated arrays composed of multiple subarrays is considered. The subarrays are assumed to compute locally the sample covariance matrices of their measurements and communicate them to the processing center. A sufficient condition for the unique identifiability of the sources in the aforementioned non-coherent processing scheme is presented. We prove that, under mild conditions, with the non-coherent system of subarrays, it is possible to identify more sources than identifiable by each individual subarray. This property of non-coherent processing has not been investigated before. We derive the maximum likelihood estimator for DOA estimation at the processing center using the sample covariance matrices received from the subarrays. Moreover, the Cramér–Rao bound (CRB) for our measurement model is derived and is used to assess the presented DOA estimators. The behavior of the CRB at high signal-to-noise ratio (SNR) is analyzed. In contrast to coherent processing, we prove that the CRB approaches zero at high SNR only if at least one subarray can identify the sources individually. [ABSTRACT FROM AUTHOR]

Direction of arrival (DoA) estimation has wide-ranging applications and is particularly challenging in complex propagation environments. Conventional approaches estimate the DoA based on the phase of the impinging signal on a sensor array. This approach has several challenges: for example, it requires tight synchronization among array elements and the estimated DoA is sensitive to multipath and to correct specification of the array geometry. In this paper, we develop a principled Bayesian DoA estimation algorithm based on the received signal strength gradient. We present algorithms for clustering, spatial- and model-based outlier detection and removal, and proper uncertainty quantification, which yield an estimator robust to non-uniform shadowing, multipath, and errors in antenna position. Physics-based full-wave simulations and experiments in the 2.4 GHz and 40 MHz bands illustrate the utility of our approach. [ABSTRACT FROM AUTHOR]

The limited space of a conformal array may lead to a serious mutual coupling effect, which will significantly affect the performance of direction of arrival (DOA) estimation algorithms. In this paper, an efficient 2-D direction finding method is developed in the presence of unknown mutual coupling for the uniform cylindrical conformal array (CCA). To avoid the time-consuming two-dimensional spectral peak searching, the 2-D DOA estimation is decoupled and divided into two 1-D DOA estimations. Elevation is first estimated based on a subarray estimation of signal parameters via rotation invariant technique (ESPRIT), and then azimuth is estimated based on the rank reduction (RARE) method by using the elevation estimation result. Consequently, the mutual coupling coefficients can be estimated after getting the DOA estimates. The proposed method can well calibrate the mutual coupling effect of a cylindrical array with a low computational complexity. The final simulation results corroborate our analysis. [ABSTRACT FROM AUTHOR]

Two-dimensional direction of arrival (2D-DOA) and polarisation estimation of multiple incident signals for linear spatially separated polarisation sensitive array (SS-PSA) is investigated with reweighted atomic norm minimisation (RAM) algorithm. Single vector sensor in this paper is composed of spatially separated three dipoles and three loops. Firstly, the received data of the proposed array with single snapshot is converted into the new received data with six virtual snapshots. Each virtual snapshot data is the data sensing by dipoles or loops located in the same direction. Secondly, based on each virtual snapshot data, the complex amplitudes and frequencies of the virtual signals are restored using RAM algorithm. Lastly, 2D-DOA and polarisation angles are derived according to the restored complex amplitudes and frequencies. Simulation results demonstrate that the RAM algorithm used in 2D-DOA and polarisation estimation with SS-PSA exhibits superior performance, which is better compared with polarisation multiple signal classification (PMUSIC) algorithm and rotational invariance techniques (ESPRIT) algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a fast range and bearing estimation method for a single near-field source is proposed. By constructing the covariance matrix of received data, the phase containing the information of range and the direction of arrival is extracted. Finally, the phase is rewritten as matrix form, and the closed-formed solution of range and bearing of the source can be calculated by least square method. In contrast to high-order statistics based methods, the proposed method can obtain the range and bearing estimation without eigenvalue decomposition and spectral search, which remarkably reduce computational complexity. Simulation results show the proposed method can provide improved estimation accuracy and higher efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a sound source localization method for simultaneously estimating both direction-of-arrival (DOA) and distance from the microphone array is proposed. For estimating distance, the off-grid problem must be overcome because the range of distance to be considered is quite broad and even not bounded. The proposed method estimates the positions based on a modified version of the convex clustering method combined with the sparse coefficients estimation. A method for constructing a suitable monopole dictionary based on the coherence is also proposed so that the convex clustering based method can appropriately estimate distance of the sound sources. Numerical and measurement experiments were performed to investigate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract Sparse array is an irregular array with a high degree of freedom in its difference coarray domain. Nested array and coprime array are two important one dimensional sparse arrays. For fixed number of sensors, nested array has a high degree of freedom, resolution and accuracy than coprime array. 1D nested arrays do not provide 2D scanning. 2D nested array which is an extension of 1D nested array provides both 2D scanning as well as high degree of freedom, however direction of arrival (DoA) estimation techniques used for 2D nested array such as 2D unitary ESPRIT and 2D spatial smoothing MUSIC has high computational complexity. This paper shows that spatial smoothing is not necessary to apply 2D MUSIC in the difference coarray domain of 2D nested array. A new Hermitian Toeplitz matrix is constructed instead of spatial smoothing. 2D MUSIC on this Hermitian Toeplitz matrix can resolve the DoA of sources with less computations. [ABSTRACT FROM AUTHOR]

Direction of arrival algorithms are used, in general, to estimate a number of incident plane waves on the antenna array and their angles of incidence. In this paper, firstly, a new approach representing a more extensive version of the conventional Capon method is proposed. The study employs the root-Capon approach based on cross-correlation matrix in extracting the estimated direction-of-arrival of some number of signals to a given uniform linear array (ULA) antenna system. Secondly, a simple modification on the antenna element's number is employed for the Capon and the proposed approach to obtain more performances. The effectiveness of the proposed algorithm is verified through numerical simulation examples, and it is shown that the root version of Capon method can provide more accurate angle estimation with less computational complexity than conventional Capon algorithm. Similarly, it is found also that the used modification plays a significant role in the performance's improvement of Capon and its variant approaches. [ABSTRACT FROM AUTHOR]

An array antenna system with innovative signal processing can enhance the resolution of a signal direction of arrival (DOA) estimation. Super resolution algorithms take advantage of array antenna structures to better process the incoming signals. They also have the ability to identify multiple targets. This paper explores the eigen-analysis category of super resolution algorithm. A class of Multiple Signal Classification (MUSIC) algorithms known as a root-MUSIC algorithm is presented in this paper. The root-MUSIC method is based on the eigenvectors of the sensor array correlation matrix. It obtains the signal estimation by examining the roots of the spectrum polynomial. The peaks in the spectrum space correspond to the roots of the polynomial lying close to the unit circle. Statistical analysis of the performance of the processing algorithm and processing resource requirements are discussed in this paper. Extensive computer simulations are used to verify the performance of the algorithm for imperfect system calibration. [ABSTRACT FROM AUTHOR]

Ray-optical methods incorporating Geometric Optics (GO) and the Uniform Theory of Diffraction (UTD) are appropriate for the representation of radiowave propagation in cities for frequencies in the UHF bend and above, where the wavelength is small compared to building dimensions. Various ray codes have been written to implement the ray representation. Because the rays undergo multiple interactions with the buildings over long distances, the codes make various assumptions to reduce running time. This paper reviews the types of assumptions that have been made, the ways in which the ray procedures have been implemented, and the accuracy that can be expected from the predictions. The paper gives examples of how the computed ray quantities have been used to simulate the characteristics of the radio channel (besides received power) that impact the designs of different radio systems. [ABSTRACT FROM AUTHOR]

• Propose one hierarchical audio augmentation chain (HAAC) for the ACCDOA-represented SELD. • Generate simulated audio mixtures for SELD and make all synthesis details disclosure. • Conduct SELD experiments with two baseline systems on two benchmark datasets. • Both HAAC and more synthesized simulation audio are helpful to improve the SELD performance. The goal of sound event localization and detection (SELD) is to detect the temporal occurrence activity of a known set of sound events and locate them in the spatial space. We argue that acquiring a large audio dataset is essential for one deep neural network-based SELD system learned as one supervised task. Nonetheless, gathering and annotating such datasets is a costly and time-intensive process. Hence, various data augmentation methods have attracted attention as a solution to increase sample diversity from the limited collections. In this paper, we propose to augment the limited audio samples for the deep neural network-based SELD system in two ways. One is the hierarchical audio augmentation chain (HAAC) proposed for the activity-coupled Cartesian direction of arrival output representation (ACCDOA) described SELD task. It consists of three waveform and spectrogram augmentation techniques, which are exquisitely assembled from the feature map augmentation to audio channel swapping, and finally sample mixup. Second, we propose to augment the training samples by generating more simulated audio samples and making the selected sound events list publicly available to the community. Experiments on the STARSS22 dataset showed that our HAAC audio augmentation chain greatly improved the SELD performance, which increased the sound event detection score by 24% and decreased the localization error by 12.1°. We demonstrate it's one simple yet effective approach, compared to other data augmentation methods. Moreover, with more simulated audio samples, generated by convolving selected sound events with SRIRs, used for training, the SELD performance was improved greatly. [ABSTRACT FROM AUTHOR]

The use of smart antenna arrays in wireless communication systems is becoming increasingly popular, as they improve the performance of the radio link and allow the integration of communication and sensing functions. In this paper, we present the design and performance evaluation of a uniform circular antenna array (UCAA) with 12 monopole antennas and RF switches to select the active antenna for the signal direction of arrival (DoA) estimation. The antenna configuration was optimized through simulations for a different number of antennas and antenna array radius. The frequency tuning of the antennas considered the coupling of the active antenna with adjacent antennas. The performance of the antenna array was evaluated by (1) simulations that examined the proximity of the antenna to the edge of the antenna array and the influence of adjacent antennas on the performance of the active antenna, and (2) measurements in outdoor and indoor environments. The results of the simulations show that antenna coupling can cause nulls in the radiation pattern, which affects the DoA estimation by reducing the signal-to-noise ratio. In a semi-controlled environment, we achieved a DoA estimation error of less than 1 degree by using the Bluetooth Low Energy (BLE) Angle of Arrival feature. [ABSTRACT FROM AUTHOR]

Sparse methods have been recently introduced in Direction-Of-Arrival (DOA) estimation as an alternative to subspace-based methods and Maximum Likelihood (ML) techniques. This paper proposes a low complexity sparse L0-regularized method for DOA estimation of coherent signals in multipath environment, a scenario in which subspace-based methods fail. The first eigenvector of the covariance matrix is used as an observation and its statistics in the presence of modeling errors are derived. Thanks to those statistics, we present a theoretical statistical analysis of an interval in which the regularization parameter, usually empirically tuned, should stand. For a regularization parameter in this interval, the global solution of the L0-regularized problem coincides with the solution of the deterministic ML. An off-line selection of the regularization parameter is therefore proposed. Simulations confirm the relevance of this interval and of the proposed method. [ABSTRACT FROM AUTHOR]

An enhanced smoothed l 0 -norm algorithm for the passive phased array system, which uses the covariance matrix of the received signal, is proposed in this paper. The SL0 (smoothed l 0 -norm) algorithm is a fast compressive-sensing-based DOA (direction-of-arrival) estimation algorithm that uses a single snapshot from the received signal. In the conventional SL0 algorithm, there are limitations in the resolution and the DOA estimation performance, since a single sample is used. If multiple snapshots are used, the conventional SL0 algorithm can improve performance in terms of the DOA estimation. In this paper, a covariance-fitting-based SL0 algorithm is proposed to further reduce the number of optimization variables when using multiple snapshots of the received signal. A cost function and a new null-space projection term of the sparse recovery for the proposed scheme are presented. In order to verify the performance of the proposed algorithm, we present the simulation results and the experimental results based on the measured data. [ABSTRACT FROM AUTHOR]

The actual underwater environmental noise is often spatial colored, which results in severe degradation of the performance of the underwater direction of arrival (DOA) estimation method based on the assumption of white noise. In the presence of Gaussian colored noise, a high-resolution DOA estimation method using a fourth-order cumulant for quickly eliminating redundancy is adopted in this paper. Firstly, a selection matrix is constructed, and the redundant data in the fourth-order cumulants are reduced in the way of descending order. Secondly, the fourth-order cumulants matrix is transformed into a vectorized form, and the selection matrix is further constructed to eliminate redundant data in the vectorization process, and a single observation vector model with better performance is obtained. Finally, the sparse representation method is used for DOA estimation. The simulation results demonstrate that compared with the traditional fourth-order cumulant methods, this method has a stronger ability to suppress colored noise, and can provide higher resolution and higher estimation accuracy under the conditions of few snapshots and low signal-to-noise ratio. The experiment verifies that this method can be applied to DOA estimation of underwater acoustic array signals.

The present paper analyzes the performance of localization systems, based on dual-band Direction of Arrival (DoA) approach, in multi-path affected scenarios. The implemented DoA estimation, which belongs to the so-called Space and Frequency Division Multiple Access (SFDMA) technique, takes advantage of the use of two uncorrelated communication carrier frequencies, as already demonstrated by the authors. Starting from these results, this paper provides, first, the methodology followed to describe the localization system in the proposed simulation environment, and, as a second step, describes how multi-path effects may be taken into account through a set of full-wave simulations. The latter follows an approach based on the two-ray model. The validation of the proposed approach is demonstrated by simulations over a wide range of virtual scenarios. The analysis of the results highlights the ability of the proposed approach to describe multi-path effects and confirms enhancements in DoA estimation as experimentally evaluated by the same authors. To further assess the performance of the aforementioned simulation environment, a comparison between simulated and measured results was carried out, confirming the capability to predict DoA performance. [ABSTRACT FROM AUTHOR]

Sparse arrays are widely employed in array signal processing due to their obvious advantages in array element distribution and uniform degrees of freedom (uDOFs). In this paper, a generalized augmented multi-subarray nested array (GAMSNA-I) and its variant, GAMSNA-II are proposed, with the objective of increasing uDOFs and reducing mutual coupling. Based on two subarrays of the prototype nested array (NA), GAMSNA-I is constructed by reconfiguring the dense uniform linear array (ULA) and forward-shifting the sparse ULA. GAMSNA-II is obtained by sparsifying the dense part of GAMSNA-I, ensuring constant uDOFs while further reducing mutual coupling. Subsequently, the closed-form expression for the uDOFs of GAMSNA-I with an arbitrary number of sensors is derived, and the proof is provided that the uDOFs of GAMSNA-II remain unchanged relative to that of GAMSNA-I. Compared to some existing array configurations, both GAMSNA-I and GAMSNA-II exhibit improved uDOFs, with GAMSNA-II achieving lower mutual coupling. Simulation results show the superior performance of the proposed GAMSNA-I and GAMSNA-II.

Abstract This paper proposes a symmetric co-prime multiple input multiple output (MIMO) configuration for direction of arrival (DOA) estimation, where the transmitter has 4 M - 1 sensors with spacing of N units and the receiver has 2 N - 1 sensors with spacing of M units. By separating the transmitter and receiver into two subarrays respectively, the symmetric sum coarray (SSC) exploits four sum coarrays based on the received signal model, and the difference coarray of SSC (DSSC) is developed by vectorizing the sample covariance matrix. The analytical expressions for the coarray aperture, the maximum consecutive integers and the attainable number of unique lags are derived carefully. Compared with the existing configurations, SSC and DSSC can obtain larger number of degrees of freedom (DOFs). Then, the DOA estimation of mutually coherent and uncorrelated targets is employed for illustration. Simulation results demonstrate the effectiveness of proposed configuration. [ABSTRACT FROM AUTHOR]

Generally, a coprime L-shaped array (CLsA) is composed of two uniform L-shaped subarrays with larger spacing among inter-element to accomplish the improved direction of arrival (DOA) estimation performance. In this paper, the two subarrays are unfolded to extend the array aperture and the performance of the unfolded CLsA (UCLsA) for two-dimensional (2D) DOA estimation is investigated. In addition, an all array multiple signals classification (AA-MUSIC) algorithm is proposed for the UCLsA. By stacking the received signals of the two subarrays, the ambiguity problem can be avoided on the basis of the coprime property. Simultaneously, due to the combination of the cross-correlation and auto-correlation, the proposed AA-MUSIC algorithm can achieve the full degrees of freedom (DOFs) and obtain more accurate DOA estimates, nevertheless, the expensive total spectral search is entailed. Consequently, a reduced complexity MUSIC (RC-MUSIC) algorithm is proposed to relieve the computational burden. The Cramer-Rao Bounds (CRBs) are utilised as a theoretical benchmark for the lower bound of unbiased estimate. Furthermore, numerical simulations verify the effectiveness and superiority of the AA-MUSIC algorithm and RC-MUSIC method for the UCLsA. [ABSTRACT FROM AUTHOR]

This paper presents a new algorithm for mixed far-field and near-field source localization using a uniform linear array (ULA). Firstly, the ULA is divided into two overlapping subarrays to construct two special cross-cumulant matrices of the subarray outputs, which are only characterized by directions-of-arrival (DOAs) of the sources. Then, the shift invariance structure in the cumulant domain is derived, and the DOAs of all sources are estimated by the TLS-ESPRIT method. Finally, with the estimated DOAs, the range estimates of near-field sources are obtained via one-dimensional search, and the types of sources are also distinguished. The developed algorithm involves neither DOA search nor parameter pairing. Furthermore, it exhibits a higher localization accuracy than the traditional methods. Simulation results are presented to demonstrate the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we addressed the problem of estimating the two-dimensional (2D) Direction of Arrival (DOA) elevation and azimuth angles for multiple sources. The proposed method employs Propagator Method (PM) in conjunction with parallel factor (PARAFAC) model using a new antenna array configuration. The proposed method overcomes two main drawbacks in the existing 2D DOA schemes: use of high computation eigenvalue decomposition (EVD) or singular value decomposition (SVD), and complex pair-matching methods for elevation and azimuth angles in case of multiple sources. Therefore, significant reduction in computational load and complexity is achieved. Computer simulations demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of coherent source localisation with multi-input multi-output (MIMO) radar via a smoothing matrix set (SMS) scheme. We first perform the transmission diversity smoothing to form some transmission sub-vectors (TSVs) and then perform reception diversity smoothing for each TSV. That is, each TSV consists of some reception sub-vectors (RSVs). By rearranging the elements of the covariance sub-matrix of each TSV along the vertical direction, both the forward-only SMS (FO-SMS) and forward backward SMS (FB-SMS) methods are derived using these RSVs. Compared with the existing smoothing methods, the proposed scheme can extract more data information from each covariance sub-matrix and also be suitable for joint direction of arrival (DOA) and direction of departure (DOD) estimation. Simulation results show that, FB-SMS can improve the estimation performance as compared to the state-of-the-art methods for monostatic or bistatic MIMO radars. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sparse arrays, such as nested arrays and coprime arrays, can achieve a high number of degrees of freedom (DOFs) for direction of arrival (DOA) estimation with a reduced number of antennas. On the other hand, the compressive measurement method provides an effective way to reduce the number of front-end circuit chains. In this paper, we generalized current works on the two categories of methods to a compressed sparse array (CSA) scheme, which combines the compressive measurement method, and the sparse array together to significantly reduce the system complexity. After introducing the proposed scheme, the Cramér-Rao bound (CRB) of the proposed CSA scheme is derived. We then determine the corresponding existing conditions of the CRB, based on which the number of DOFs is derived and examined for the first time. It is proved that, for a CSA which compressed the output of an $L$ -element sparse array to $M < L$ chains, a higher number of DOFs is obtained as compared to that of the $M$ -element array with the same sparse structure. Furthermore, the DOA estimation accuracy using the $M$ -chain CSA is higher than that using the $M$ -element sparse array due to the extended array aperture. Numerical simulations verify the superiority of the proposed CSA scheme. [ABSTRACT FROM AUTHOR]