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One of the most important field in array signal processing is direction-of-arrival (DOA) estimation, which is applicable in wireless and mobile communications. We consider the problem of estimating the DOA for reactance-based uniform circular arrays (UCAs). In this paper, a new technique of estimating DOAs is proposed for the reactance-based UCA antenna as well as a method to calculate optimal reactance values. This technique is based on the modified reactance domain MUltiple Signal Classification (MUSIC) algorithm. Simulation results show that DOAs can be estimated by the active ports reactance domain MUSIC (APRD-MUSIC) algorithm for reactance-based UCA antennas. By optimizing the reactance values, the proposed system obtains good performance in estimating DOAs in terms of resolution and error estimation. Simulation and measurement results show that high-resolution DOA estimation can be achieved by using APRD-MUSIC algorithm for Reactance based UCA antennas. [ABSTRACT FROM PUBLISHER]

An acoustic sea glider has been developed for ambient sea noise measurement and target detection through the deployment of an acoustic vector sensor (AVS). The glider was designed with three cabins connected in sequence and it can dive to depths exceeding 1200m. The AVS fixed on the glider measure acoustic pressure and particle velocities related to undersea noise, and the inner attitude sensors can effectively eliminate the estimation deviation of the direction of arrival. The inherent self-noises of the acoustic sea glider and AVS are presented respectively in respect to the Knudsen spectra of sea noise. Sea trial results indicate that the AVS could work well for undersea noise measurement when the glider is smooth sliding, and the target azimuth estimated by AVS after correction is remarkably consistent with the values measured by the GPS, and direction-finding errors are less than 10 degrees. The research in this paper shows that the acoustic sea glider is able to undertake tasks such as a wide range of underwater acoustic measurement and detection. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) is increasingly important for exploring the real world, which has partially moved to virtual workplaces. In order to create immersive presence in a simulated scene for humans, VR needs to reproduce spatial audio that describes three-dimensional acoustic characteristics in the counterpart physical environment. When the user moves, this reproduction should be dynamically updated, which provides practical challenges because the bandwidth for continuously transmitting audio and video scene data may be limited. This paper proposes an interpolation approach for dynamic spatial audio reproduction using acoustic characteristics of direction and reverberation at limited numbers of positions, which are represented using a first order Ambisonics encoding of the room impulse response (RIR), called the directional RIR (DRIR). We decompose two known DRIRs into reflection components, before interpolating early dominant components for DRIR synthesis and utilizing DRIR recordings for accuracy evaluation. Results indicate that the most accurate interpolation is obtained by the proposed method over two comparative approaches, particularly in a simulated small room where most direction of arrival estimation errors of early components are below five degrees. These findings suggest precise interpolated DRIRs with limited data using the proposed approach, which is vital for dynamic spatial audio reproduction for VR applications. [ABSTRACT FROM AUTHOR]

Direction of arrival estimation of LFM signal is an essential task in radar, sonar, acoustics and biomedical. In this paper, a short time Fourier transform multi-step knowledge aided iterative generalized minimum residual (STFT-MS-KAI-GMRES) approach is presented to amend the angle measurement of this signal. A three stage algorithm is proposed. First, the process is initiated with formulating an estimation algorithm for the carrier frequency and chirp rate, followed by calculation of STFT of the output of array element; this yields a spatial time–frequency distribution matrix. Next, the Krylov subspace-based estimation algorithm is formulated in the presence of MS-KAI-ESPRIT algorithm. If the number of antennas increases, the accuracy of the algorithm will increase, but we will incur more communication costs. Results are presented showing attainment of the CRLB by STFT-MS-KAI-GMRES the for an adequately large signal to noise ratio. An important feature of the method presented in the current study is the low computational complexity that has higher suitability for practical applications. [ABSTRACT FROM AUTHOR]

The sparse Multiple-Input–Multiple-Output radar has large array element spacing, which has a great significance for reducing the mutual coupling effects. In this paper, the authors propose a compensation sparse arrays with flexible inter-element spacing (CSA-FIS). Compared with the sparse arrays with flexible inter-element spacing (SA-FIS), the CSA-FIS improves the situation in that the number of virtual sensors significantly decreases when the inter-element spacing of two sub-array is not coprime. Under the guarantee of lower mutual coupling effects, the system degrees of freedom (DOFs) and the number of consecutive virtual sensors are also improved. Meanwhile, a pre-processing DFT-compressed sensing (D-CS) algorithm is proposed, with the purpose to solve the problem that the traditional CS algorithm has a high calculation cost when all non-zero parameters must be estimated. Numerical simulations demonstrate the superiority of the CSA-FIS in terms of DOFs and estimation accuracy when compared with SA-FIS. Moreover, compared with the traditional CS algorithm, the D-CS has lower computational complexity. [ABSTRACT FROM AUTHOR]

This paper discusses the problem of two-dimensional direction of arrival estimation of coherent sources for acoustic vector sensors array. Compared with subspace-based methods, such as root multiple signal classification and estimation of signal parameters via rotational invariance technique, the propagator method (PM) has lower computational complexity. However, only in high-snapshots situation, can the PM algorithm enjoy a better estimation performance. Besides, all of these algorithms mentioned above cannot work for coherent sources. In this paper, we combine PM algorithm with Toeplitz Hermitian matrix reconstruction, and propose an improved algorithm, which works well in the case of coherent signals and a single snapshot. Furthermore, the proposed method can achieve automatically paired two-dimensional angle estimation. Simulation results verify that the proposed method has the better angle performance and less computational complexity than spatial smoothing methods. [ABSTRACT FROM AUTHOR]

In this paper, we study blind channel-and-signal estimation by exploiting the burst-sparse structure of angular-domain propagation channels in massive MIMO systems. The state-of-the-art approach utilizes the structured channel sparsity by sampling the angular-domain channel representation with a uniform angle-sampling grid, a.k.a. virtual channel representation. However, this approach is only applicable to uniform linear arrays and may cause a substantial performance loss due to the mismatch between the virtual representation and the true angle information. To tackle these challenges, we propose a sparse channel representation with a super-resolution sampling grid and a hidden Markovian support. Based on this, we develop a novel approximate inference based blind estimation algorithm to estimate the channel and the user signals simultaneously, with emphasis on the adoption of the expectation-maximization method to learn the angle information. Furthermore, we demonstrate the low-complexity implementation of our algorithm, making use of factor graph and message passing principles to compute the marginal posteriors. Numerical results show that our proposed method significantly reduces the estimation error compared to the state-of-the-art approach under various settings, which verifies the efficiency and robustness of our method. [ABSTRACT FROM AUTHOR]

An array's constituent sensors could be spatially dislocated from their nominal positions. This paper investigates how such sensor dislocation would degrade a uniform circular array (UCA) of isotropic sensors (like pressure sensors) in their direction-finding precision. This paper analytically derives this direction finding's hybrid Cramér-Rao bound (HCRB) in a closed form that is expressed explicitly in terms of the sensors' dislocation parameters. In the open literature on UCA direction finding, this paper is the first to be three-dimensional in modeling the sensors' dislocation. Perhaps unexpectedly to some readers, sensor dislocation could improve and not necessarily degrade the HCRB; these opposing effects depend on the dislocation variances, the incident source's arrival angle, and the signal-to-noise power ratio—all analyzed rigorously in this paper. Interesting insights are thereby obtained: (a) The HCRB is enhanced for the impinging source's polar arrival angle as the sensors become more dislocated along the impinging wavefront due to aperture enlargement over the stochastic dislocation's probability space. (b) Likewise, the HCRB is improved for the azimuth arrival angle as the sensors become more dislocated on the circular array's plane, also due to aperture enlargement. (c) In contrast, sensor dislocation along the incident signal's propagation direction can only worsen the HRCBs due to nuisance-parameter effects in the Fisher information. (d) Sensor dislocation orthogonal to the array plane must degrade the HCRB for the azimuth arrival angle but could improve the HCRB for the polar arrival angle. [ABSTRACT FROM AUTHOR]

Switched beamforming using electronic phase shifters is commonplace. Digital switched beamformers offer a premise of better performance than electronic phase shift switched beamformers. It is also worth noting that current unknown signal Direction of Arrival (DoA) estimation methods (commonly MUltiple SIgnal Classification (MUSIC) and Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)) are generally computationally intensive. In this paper, signal DoA estimation and digital switched beamforming using aptly designed Artificial Neural Network (ANN) classifiers are looked into. Initially, signals detected at a rectangular receiving array are mapped onto a DoA through an ANN classifier. A second ANN classifier maps the selected DoA onto an optimal set of beamforming weights leading to an optimal switched beamforming reception pattern. The ANN classifiers' performance in DoA estimation and beamforming is tested over a variety of trials, yielding good results. The designed ANN beamformer premises to yield high-speed and accurate switched beamforming performance, most notably in large array systems. The ANN DoA estimator/beamformer can be easily adapted to nonuniform arrays wherein closed form DoA estimation/beamforming solutions are impractical. MATLAB software environment has been used as the main analysis tool. [ABSTRACT FROM AUTHOR]

In sparsity-based optimization problems, one of the major issue is computational complexity, especially when the unknown signal is represented in multi-dimensions such as in the problem of 2-D (azimuth and elevation) direction-of-arrival (DOA) estimation. In this paper, a low-complexity sparsity-based method is proposed for DOA estimation in the presence of array imperfections such as mutual coupling. In order to reduce the complexity of the optimization problem, this paper introduces a new sparsity structure that can be used to model the optimization problem in case of multiple data snapshots and multiple separable observations where the dictionary can be decomposed into two parts: azimuth and elevation dictionaries. The proposed sparsity structure is called joint-block-sparsity which exploits the sparsity in both multiple dimensions, namely azimuth and elevation, and data snapshots. In order to model the joint-block-sparsity in the optimization problem, triple mixed norms are used. In the simulations, the proposed method is compared with both sparsity-based techniques and subspace-based methods as well as the Cramer–Rao lower bound. It is shown that the proposed method effectively calibrates the sensor array with significantly low complexity and sufficient accuracy. [ABSTRACT FROM AUTHOR]

This paper considers the direction of arrival (DOA) estimation of coherent and uncorrelated targets by exploiting a flexible multiple input multiple output radar. First, the proposed radar is generalized with two coprime expansion factors for enlarging the inter-element spacing of transmit and receive arrays, referred to as sparse arrays with flexible inter-element spacing (SA-FIS), which shows that the conventional nested or coprime ones are the special cases. The range of consecutive lags and the number of unique lags in the sum-difference coarray are derived in closed form. It is verified that SA-FIS can obtain the maximum unique lags and also suppress the mutual coupling effects. We then extend the FIS viewpoint to sparse nonuniform linear arrays, where the two-level nested and coprime arrays are employed to achieve a significant increase in degrees of freedom (DOFs). Furthermore, to fully utilize these unique lags, we propose a reduced-complexity two-step sparse representation algorithm. By modifying and removing the off-diagonal elements of the estimated target covariance matrix, the proposed method can just identify the diagonal ones, thereby leading to further improved performance with much lower computational complexity. Finally, the Cramér-Rao bound on DOAs and correlated coefficients for SA-FIS is derived. Numerical simulations demonstrate the superiority of the proposed method with SA-FIS in terms of DOFs, computations, estimation accuracy, and resolution capability compared with previous ones. [ABSTRACT FROM AUTHOR]

Fiber-optic interferometric sensing is a very attractive approach for creating lightweight, high-sensitive, and fully passive fiber-optic hydrophone arrays. In this paper, results of sea trials of the thin cable fiber-optic hydrophone array for passive acoustic surveillance applications are presented. The performed trials consisted of two parts: the first part was devoted to the estimation of the array acoustic performance and the second part included the direction of arrival (DOA) estimation of the moving acoustic source. The estimation of the array acoustic performance showed that the average measured pressure sensitivity of fiber-optic hydrophones increased as the acoustic frequency increased from −122 dB re rad/ $ {\mu }$ Pa at 20 Hz to −116 dB re rad/ ${\mu }$ Pa at 495 Hz. The average minimum detectable pressure of hydrophones decreased from 78, 1 dB re $1~{\mu }$ Pa at 20 Hz to 28, 5 dB re $1~{\mu }$ Pa at 495 Hz. Obtained values of the minimum detectable pressure were below the sea state zero noise level in the considered frequency range. The average dynamic range of fiber-optic hydrophones was above 120 dB at 495 Hz. The proper capability of the DOA estimation was successfully demonstrated using the motor boat as the moving acoustic source. Trajectories of the moving boat were obtained by using the beamscan DOA estimation algorithm with additional Kalman filtering. Obtained results demonstrated the great potential of using fiber-optic interferometric sensors for hydroacoustic surveillance applications. The presented results might be helpful for the implementation of extended fiber-optic hydroacoustic surveillance systems. [ABSTRACT FROM AUTHOR]

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

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]

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

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]

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]

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]

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]

This paper presents a single-pixel polarimetric compressive sensing (CS)-based direction of arrival (DoA) estimation technique using a cavity backed programmable coding metasurface aperture. The single-pixel DoA retrieval technique relies on a dynamically modulated waveform diversity, enabling spatially incoherent radiation masks to encode the incoming plane waves on the radar aperture using a single channel. The polarimetric nature of the wave-chaotic coded metasurface ensures that the DOA estimation is sensitive to the polarization state of the incoming waves. We show that the polarimetric single-pixel DoA concept can be realized by encoding the polarization information of the incoming waves at the physical layer level within the antenna. A dynamically reconfigurable wave-chaotic metasurface, which possesses a structured sparsity of dual-polarized coded metamaterial elements, is proposed for the proof of concept. It is shown that by encoding and compressing the source generated far-field incident waves into a single channel, we can retrieve high fidelity polarimetric DoA information from compressed measurements. [ABSTRACT FROM AUTHOR]

When the intersensor spacing of a uniform linear array (ULA) is larger than the half-wavelength of an incident narrowband signal, spatial aliasing is generated. For broadband signals, the broadband spatial spectrum is still affected as a result of the spatial aliasing in each frequency bin. In this paper, an aliasing-free broadband direction-of-arrival (DOA) estimation algorithm for ULAs is proposed. First, an array output is constructed with a given Gaussian random sequence from the direction ϑ. Then, a frequency-difference (FD) operation is conducted, which multiplies the array observation in the frequency bin f by the conjugate form of the constructed array output in the frequency bin f + Δ f. Thus, an equivalent array output at a desired frequency Δ f is obtained, whose wavelength is equal to twice the intersensor spacing. In this manner, an aliasing-free spatial spectrum in the FD domain is achieved. Scanning the direction ϑ , the DOA of signals is finally estimated based on the difference between the peaks in the aliasing-free spatial spectrum and direction ϑ. The proposed method can achieve a satisfactory estimation even in a strong interference environment. The simulations and experimental results are included to demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates estimation of direction of arrivals (DOAs) and time of arrivals (TOAs) for a multi-user orthogonal frequency-division multiplexing (OFDM) system that is equipped with large-scale arrays and interfered by carrier frequency offsets (CFOs). We propose a novel scheme for joint TOA and DOA estimation with CFO compensation by taking advantages of large-scale receive antenna arrays. The pilot is designed to balance the pilot length and the estimation performance. The proposed CFO estimation scheme is with lower complexity. After analyzing the resolution of joint DOA and TOA estimation, we find a criterion to choose an appropriate smoothing window for the best resolution and provide insights in sub-carrier selection strategy. We also prove that the large-scale array can asymptotically achieve perfect resolution of both TOA and DOA estimation. We reveal the pseudo peak (PP) elimination property of large-scale arrays and exploit it to develop a novel path number estimation algorithm accordingly. Based on our numerical results, the proposed scheme can efficiently eliminate the CFO effect, the optimized smoothing window attains much better performance, and the path number estimation algorithm performs better than the traditional minimum description length (MDL) approach, especially in the low signal-to-noise ratio (SNR) regime. [ABSTRACT FROM AUTHOR]

This paper presents a convolution neural network (CNN)-based direction of arrival (DOA) estimation method for radio frequency (RF) signals acquired by a nonuniform linear antenna array (NULA) in unmanned aerial vehicle (UAV) localization systems. The proposed deep CNN, namely RFDOA-Net, is designed with three primary processing modules, such as collective feature extraction, multi-scaling feature processing, and complexity-accuracy trade-off, to learn the multi-scale intrinsic characteristics for multi-class angle classification. In several specific modules, the regular convolutional and grouped convolutional layers are leveraged with different filter sizes to enrich diversified features and reduce network complexity besides adopting residual connection to prevent vanishing gradient. For performance evaluation, we generate a synthetic signal dataset for DOA estimation under the multipath propagation channel with the presence of additive noise, propagation attenuation and delay. In simulations, the effectiveness of RFDOA-Net is investigated comprehensively with various processing modules and antenna configurations. Compared with several state-of-the-art deep learning-based models, RFDOA-Net shows the superiority in terms of accuracy with over 94% accuracy at 5 dB signal-to-noise ratio (SNR) with cost-efficiency. [ABSTRACT FROM AUTHOR]

We consider a modern radar system which has a general antenna array configuration consisting of primary channels with high-gain beams and reference channels with low-gain beams. A generalized likelihood ratio test (GLRT) was proposed in this context for target detection and direction of arrival estimation. However, the analytical performance of the GLRT has not been provided yet. In this paper, we derive finite-sum expressions for the probabilities of false alarm and detection. It is proven that the GLRT exhibits a constant false alarm rate against the overall interference disturbance covariance matrix. Numerical examples show that the detection performance of the GLRT may not necessarily improve when the number of sensors increases [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of wideband direction of arrival (DoA) estimation with sparse linear arrays (SLAs), where a number of uncorrelated wideband signals impinge on an SLA and the data is collected from multiple frequency bins. To boost the performance and perform underdetermined DoA estimation, the difference co-array response matrices for all frequency bins are constructed first. Then, to merge the data from different frequency bins, we resort to the Jacobi-Anger approximation to transform the co-array response matrices of all frequency bins into a single virtual uniform linear array (ULA) response matrix. The major advantage of this approach is that the transformation matrices are all signal independent. For the special case where all sources share an identical distribution of the power spectrum, we develop two super-resolution off-the-grid DoA estimation approaches based on atomic norm minimization (ANM), one with and one without prior knowledge of the power spectrum. Our solution is able to resolve more sources than the number of antennas but also more than the number of degrees of freedom (DoF) of the difference co-array of the SLA. For the general case where each source has an arbitrary power spectrum, we propose a multi-task ANM method to exploit the joint sparsity from all frequency bins. Simulation results show that our proposed methods present a clear performance advantage over existing methods, and achieve an estimation accuracy close to the associated Cramér-Rao bounds (CRBs). [ABSTRACT FROM AUTHOR]

Modern-day, global, and clever array systems have several distinguished approaches with regard to a wireless device. In this paper, a comparative analysis has been carried out amongst nine direction of arrival (DOA) algorithms: Capon, multiple signal classification (MUSIC), Bartlett, Pisarenko, linear prediction, maximum entropies, and min-norm, root- MUSIC, and estimation of signal parameters via rotational invariance technique (ESPRIT). in this paper reduces the number of antennas and snaps necessary to observe six target swithout interference for several DOA estimation methods. After comparing and analyzing all the algorithms, it has been shown that the music algorithm is the best algorithm that reduces interference and detects the desired sources. MATLAB R2019b was used in the simulation process. [ABSTRACT FROM AUTHOR]

Direction finding for multiple targets is one of the most important tasks in MIMO radar applications. The existing algorithms such as MUSIC and ESPRIT for direction finding can only achieve the localization of multiple targets with unchanged location parameters due to their batch mode. In this paper, we show that the multiple targets localization and tracking in a bistatic MIMO radar system can be solved by adaptive asymmetric joint diagonalization, where the fluctuation of target radar cross section (RCS) is regarded as a source of time diversity for matched data, and the algebraic structure of the matched data themselves rather than their statistics is exploited. The resulting algorithm can achieve not only the localization but also tracking of multiple targets since it estimates the target location parameters in a recursive mode. The identifiability of the proposed algorithm is analyzed and the Cramér–Rao lower bound is also derived as performance benchmark. Simulation results show that the proposed algorithm has comparable localization accuracy to batch algorithms. Moreover, it exhibits good tracking capability for multiple targets with varying location parameters. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of two-dimensional (2D) direction of arrival (DOA) estimation for monostatic multiple-input multiple-output (MIMO) radar equipped with arbitrary arrays manifold. A low-complexity multi-way compressive sensing (MCS) 2D DOA estimation algorithm is derived in this paper. To ease the computational burden, the received data are first arranged into a trilinear model, and then three random compression matrixes are individually applied to reduce each tensor to a much smaller tensor. Finally, the 2D DOA estimation problem then links to the low-dimensional trilinear model. The proposed algorithm has estimation performance close to that of the trilinear decomposition algorithm while it is more appealing form the perspective of storage capacity. Compared with other estimation algorithms, such as multiple signal classification, our MCS algorithm requires neither eigenvalue decomposition of the received signal covariance matrix nor spectral peak searching. It also doesn't require array interpolation of the array geometry, therefore the proposed algorithm has much less computational load, which indicates the proposed algorithm will have wide application in the future for Massive MIMO systems. [ABSTRACT FROM AUTHOR]

In this paper, a reduced-dimension based direction of arrival (DOA) and direction of departure (DOD) estimator with real-valued implementation is presented by introducing a preprocessing transformation for bistatic multiple-input multiple-output radars is addressed. First, a two-dimensional searching estimator by exploiting unitary multiple signal classification (UMUSIC) technique with automatic pairing is presented. Second, this paper also presents a reduced-dimension (RD) UMUSIC (RD-UMUSIC) estimator by utilizing the characteristic of Kronecker product, which only requires a one-dimensional search for DOA estimation. Following this, the angle of DOD corresponding to the estimated DOA is obtained by using a shaping DOD steering vector approach. Furthermore, the DOA and DOD pairing is given automatically. The proposed estimator has low computational complexity due to the preprocessing unitary transformation and RD approaches. Finally, simulation results are presented to verify the efficiency of the proposed estimator. [ABSTRACT FROM AUTHOR]

In this paper, joint direction-of-departure (DOD) and direction-of-arrival (DOA) estimation for bistatic multiple-input–multi-output (MIMO) radar in an unknown spatially correlated noise environment is investigated. The signal model is based on the assumption that the waveforms are transmitted by two separated subarrays having $M_1$ and $M_2$ sensors and received by two separated subarrays having $N_1$ and $N_2$ sensors, respectively. The received data are pulse-compressed using a matching matrix consisting of $M=M_1+M_2$ orthogonally transmitted waveforms. The joint covariance matrix of unknown correlated noise is analyzed. A novel algorithm is proposed by jointly estimating the DOD and DOA with transmitter and receiver subarrays in unknown noise. The joint estimation algorithm is based on the canonical correlation decomposition (CCD) and exploits the shift-invariance properties in the Kronecker product structure of each column of the various steering matrices. The estimated DOA and DOD can be automatically paired correspondingly. In addition, the formulas of stochastic Cramér–Rao bounds (CRB) for DOD and DOA estimation are derived. Simulations show that our method can effectively improve the performance of estimation in unknown correlated noise environments and is insensitive to having different noise environments in the two subarrays. [ABSTRACT FROM AUTHOR]

We consider the problem of direction of arrival (DOA) estimation based on a nonuniform linear nested array, which is known to provide $O(N^2)$ degrees of freedom (DOFs) using only $N$ sensors. Both subspace-based and sparsity-based algorithms require certain modeling assumptions, e.g., exactly known array geometry, including sensor gain and phase. In practice, however, the actual sensor gain and phase are often perturbed from their nominal values, which disrupts the existing DOA estimation algorithms. In this paper, we investigate the self-calibration problem for perturbed nested arrays, proposing corresponding robust algorithms to estimate both the model errors and the DOAs. The partial Toeplitz structure of the covariance matrix is employed to estimate the gain errors, and the sparse total least squares (STLS) is used to deal with the phase error issue. In addition, we provide the Cramér-Rao bound (CRB) to analyze the robustness of the estimation performance of the proposed approaches. Furthermore, we extend the calibration strategies to general nonuniform linear arrays. Numerical examples are provided to verify the effectiveness of the proposed strategies. [ABSTRACT FROM PUBLISHER]

Direction of arrival (DOA) estimation for a sparse channel has attracted serious attention recently. Better signal analysis and denoising achieve accuracy in DOA determination. This paper proposes an underdetermined DOA estimation for multiple input and multiple outputs (MIMO) sparse channels. A novel multi-kernel-based non-negative sparse Bayesian learning (MK NNSBL) framework is implemented using the multiplied form of basis vector within the manifold matrix for a defined grid. Meanwhile, virtual antenna locations are reconfigured by exploiting the conventional cuckoo search algorithm (CCSA) for the fine reception of incoming signals on a nonuniform linear array (NULA). The simulated results reveal that the novel approach outperforms in its optimal root mean square error (RMSE) for various signal-to-noise ratio (SNR) limits and the compilation time. The convergence comparative graph indicates the improved performance in the proposed framework over existing algorithms. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of two-dimensional (2D) direction of arrival estimation of coherent sources with less computational complexity using sparse arrays. The proposed method incorporates the unitary ESPRIT method into three parallel nested and coprime arrays configuration. The advantages of the proposed method are computationally efficient due to real-valued operations, automatic pairing of 2D angles, resolves coherent targets, large array aperture and less mutual coupling. Simulation results show the effectiveness of the proposed method. Cramer–Rao lower bound (CRLB) has been given for the reference. The performance of the proposed method is compared with the methods available in the existing literature. The results achieved by the proposed method were found to be significant in terms of estimation accuracy, resolution, and the computational complexity. [ABSTRACT FROM AUTHOR]

This paper presents a method for blind separation and DOA estimation of narrow-band independent signals based on eigenvalue decomposition. It can effectively improve the blind separation performance of array mixed signals under the condition of low signal-to-noise ratio. At the same time, the DOA of the separated signal is estimated. It is of great significance to improve the adaptability of array system in complex electromagnetic signal environment. [ABSTRACT FROM AUTHOR]

In this paper, the problem of direction of arrival estimation is addressed by employing Bayesian learning technique in sparse domain. This paper deals with the inference of sparse Bayesian learning (SBL) for both single measurement vector (SMV) and multiple measurement vector (MMV) and its applicability to estimate the arriving signal's direction at the receiving antenna array; particularly considered to be a uniform linear array. We also derive the hyperparameter updating equations by maximizing the posterior of hyperparameters and exhibit the results for nonzero hyperprior scalars. The results presented in this paper, shows that the resolution and speed of the proposed algorithm is comparatively improved with almost zero failure rate and minimum mean square error of signal's direction estimate. [ABSTRACT FROM AUTHOR]

Multiple input and multiple output (MIMO) radar systems have advantages over traditional phased-array radar in resolution, parameter identifiability, and target detection. However, the estimation performance of the direction of arrivals (DOAs) and the direction of departures (DODs) will be significantly degraded for a colocated MIMO radar system with unknown mutual coupling matrix (MCM). Although auxiliary sensors (AS) can be set to solve this problem, the computational cost of two-dimensional multiple signal classification (2D-MUSIC) is still large. In this paper, a new angle estimation method is proposed to reduce the computational complexity. First, a local-search range is defined for each initial angle estimation obtained by the MUSIC with AS method. Second, the new estimation of DOAs and DODs of the targets is estimated via the joint estimation theory of angle and mutual coupling coefficient in the local search area. Simulation results validate that the proposed method can obtain the same precision and have the advantage over the global searching in computational complexity. [ABSTRACT FROM AUTHOR]

Cramér-Rao bound (CRB) has been formulated in earlier work for linear, planar and 3-D array configurations. The formulations developed in prior work, make use of the standard spatial data model. In this paper, the existence of CRB for the spherical harmonics data model is first verified. Subsequently, an expression for stochastic CRB is derived for direction of arrival (DOA) estimation in spherical harmonics domain. The stochastic CRBs for azimuth and elevation are plotted at various signal to noise ratios (SNRs) and snapshots. It is noted that a lower bound on the CRB is attained at high SNR. A similar observation is made when larger number of snapshots are used. [ABSTRACT FROM AUTHOR]

In this paper, we propose a coaxial-fed dipole array antenna in a borehole with ferrite loading to achieve a thin directional borehole radar sonde (diameter of 54 mm). This radar works between about 20 and 150 MHz. When the dipole array, proposed previously, is used for the thin radar sonde, this antenna suffers from scattered waves from the centered conducting cylinder (CCC). This disturbs the estimation of the direction of arrival (DOA) below 100 MHz. To overcome the interference of the CCC, commercial ferrite cores were added to the CCC. A method-of-moment analysis showed that we may decrease the resonant frequency of the CCC to below 20 MHz, making use of the inductive characteristics of the ferrite cores. This widens the frequency bandwidth of the antenna. Field experiments in soil showed that the ferrite core-loaded CCC works reliably and confirmed that we can estimate the DOA between 20 and 150 MHz. Other measurements in rock which examined the power spectrum of a direct wave found that the ferrite cores widened the operating frequency bandwidth of the radar system. [ABSTRACT FROM PUBLISHER]

ABSTRACT This paper presents practical design of a smart antenna system based on direction-of-arrival estimation and Dolph-Chebyshev beam forming. Direction-of-arrival estimation is based on the multiple signal classification algorithm for identifying the directions of the source signals incident on the sensor array comprising the smart antenna system. The smart antenna system design involves a hardware part, which provides real data measurements of the incident signals received by the sensor array. This paper presents the Dolph-Chebyshev method for the synthesis and design of antenna arrays with periodic element spacing. A Field-Programmable Gate Array implementation is presented for an antenna array application employing digital beamforming. The array comprises 10 elements, equal in number receiving radio frequency and intermediate frequency components, as well as a Spartan-3E Field-Programmable Gate Array-based unit, which is responsible for the control of the array. Low-cost and switched-beam, and fully adaptive antenna array suitable for 2-GHz applications are proposed in this paper. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In traditional compressed sensing theory, the dictionary matrix is given a priori, whereas in real applications this matrix suffers from random noise and fluctuations. In this paper, we consider a signal model where each column in the dictionary matrix is affected by a structured noise. This formulation is common in direction-of-arrival (DOA) estimation of off-grid targets, encountered in both radar systems and array processing. We propose to use joint sparse signal recovery to solve the compressed sensing problem with structured dictionary mismatches and also give an analytical performance bound on this joint sparse recovery. We show that, under mild conditions, the reconstruction error of the original sparse signal is bounded by both the sparsity and the noise level in the measurement model. Moreover, we implement fast first-order algorithms to speed up the computing process. Numerical examples demonstrate the good performance of the proposed algorithm and also show that the joint-sparse recovery method yields a better reconstruction result than existing methods. By implementing the joint sparse recovery method, the accuracy and efficiency of DOA estimation are improved in both passive and active sensing cases. [ABSTRACT FROM PUBLISHER]

This paper proposes a Bayesian method to address the farfield narrowband direction-of-arrival (DOA) estimation problem with time-varying arrays, whose elements relatively move in an arbitrary but known way. The measurements associated with different array geometries are formulated with distinct and spatially overcomplete observation systems, and a joint Bayesian model is established to combine those measurements and yield unified DOA estimates. The joint reconstruction process of the multiple measurements falls into the multitask learning category; thus, the proposed method is named DOA estimation via multitask learning (DEML). Theoretical results focusing on the uniqueness of the solution and the global convergence of the Bayesian learning process are also given, which indicate the maximal separable signal number and the global convergence of the proposed method in the considered array processing scenarios. Numerical examples are also provided to demonstrate the DOA estimation performance of the proposed method and support the theoretical results. [ABSTRACT FROM PUBLISHER]

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]

Location of Partial Discharge (PD) in transformers is very important, and many methodologies that have been carried forward in previous decades have a restriction theoretically, specially, in power transformers they cannot locate multiple PDs. Based on array signal processing a novel multiple partial discharges localization is presented in this paper. Ultrasonic signal emission occurs when the partial discharge occurs inside the transformer oil tank. Using ultrasonic sensor arrays located on the wall of the transformer tank, emitted ultrasonic signals is detected. Generally a single partial discharge will not occur in transformer there may be multiple partial discharge occur at different location. For detection of multiple partial discharges, we have used circular array of ultrasonic sensors (CAUS) in our proposed method. Primarily, by using Translation Invariant Wavelet Transform the partial discharge signals are de noised. To change the wide-band signal received by CAUS to the narrowband one Total Least Square algorithm is utilized. After this, the number of PD signals is estimated and to find the directions of arrival (DOA) of the signal sparse representation of array covariance vector technique is utilized. Lastly, on the basis of estimation of DOA position of PD source is detected with the help of optimization algorithm in order to find the exact coordinates of the PD source. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation algorithms based on sparse Bayesian inference (SBI) can effectively estimate coherent sources without recurring to extra decorrelation techniques, and their estimation performance is highly dependent on the selection of sparse prior. Specifically, the specified sparse prior is expected to concentrate its mass on the zero and distribute with heavy tails; otherwise, these algorithms may suffer from performance degradation. In this paper, we introduce a new sparse-encouraging prior, referred to as “Gauss-Exp-Chi2” prior, and develop an efficient DOA estimation algorithm for a mixture of uncorrelated and coherent sources under a hierarchical SBI framework. The Gauss-Exp-Chi2 prior distribution exhibits a sharp peak at the origin and heavy tails, and this property makes it an appropriate prior to encourage sparse solutions. A three-layer hierarchical sparse Bayesian model is established. Then, by exploiting variational Bayesian approximation, the model parameters are estimated by alternately updating until Kullback-Leibler (KL) divergence between the true posterior and the variational approximation becomes zero. By constructing the source power spectra with the estimated model parameters, the number and locations of the highest peaks are extracted to obtain source number and DOA estimates. In addition, some implementation details for algorithm optimization are discussed and the Cramér-Rao bound (CRB) of DOA estimation is derived. Simulation results demonstrate the effectiveness and favorable performance of the proposed algorithm as compared with the state-of-the-art sparse Bayesian algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel direction of arrival (DOA) and polarization estimation method to address the problem of a coprime polarization-sensitive array (PSA). For a PSA, there may be a zero element in the covariance matrix when the polarized signal comes from a specific direction. To overcome this problem, we utilize the reconstructed received data to obtain a new covariance matrix whose elements are all non-zero. Then, the coprime MUSIC and sparse signal reconstruction algorithms are used for DOA estimation. In addition, the power of noise can be estimated in this polarization model, which improves upon the sparse signal reconstruction algorithm. Compared with the normalized algorithm, the proposed method offers favorable performance in terms of accuracy. Furthermore, our method can identify the peaks of the true DOAs at a low signal-to-noise ratio (SNR). The simulation results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of array signal processing, direction of arrival (DOA) estimation is a prime area of research. DOA estimation and adaptive beamforming (ABF) are the main issues in smart antennas, which dynamically find the direction of desired and interfering users and finds the optimum weights for beamforming. There are numerous spectral and eigen structure algorithms for estimating the direction of narrow band sources. However, in a complex multipath channel environment, received signals from different directions are fully or partially correlated, which prevents the applications of high resolution techniques to estimate the direction of incoming signals. Maximum likelihood (ML) is an efficient technique for DOA estimation in a low signal to noise ratio (SNR) and coherent channel environment. In this paper, we use particle swarm optimization (PSO) for estimating ML solution by optimizing complex non linear multimodal function over a high dimensional space in linear arrays. PSO-ML estimator has been compared with conventional DOA estimation techniques in uncorrelated, partially correlated and coherent channel environment. The performance of PSO-ML estimator and conventional algorithms are analyzed in varying partially correlated channel environment. The simulation results demonstrate that PSO based estimator gives superior statistical performance. [ABSTRACT FROM AUTHOR]

Nested array can expand the degrees of freedom (DOF) from difference coarray perspective, but suffering from the performance degradation of direction of arrival (DOA) estimation in unknown non-uniform noise. In this paper, a novel diagonal reloading (DR) based DOA estimation algorithm is proposed using a recently developed nested MIMO array. The elements in the main diagonal of the sample covariance matrix are eliminated; next the smallest MN-K eigenvalues of the revised matrix are obtained and averaged to estimate the sum value of the signal power. Further the estimated sum value is filled into the main diagonal of the revised matrix for estimating the signal covariance matrix. In this case, the negative effect of noise is eliminated without losing the useful information of the signal matrix. Besides, the degrees of freedom are expanded obviously, resulting in the performance improvement. Several simulations are conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]