Sparse Array Research Articles

Concentric Elliptical Antenna Array Synthesis using Sine Cosine Algorithm for Reducing the Sidelobe level

This paper employs the Sine Cosine Algorithm, a recent meta-heuristic method, to enhance the radiation properties of concentric elliptical antenna arrays in far-field applications, such as smart grid wireless communication and the Internet of Things. It focuses on minimizing the Sidelobe Level, a key parameter in antenna radiation, to mitigate unwanted signals and interference. The proposed method, known for its global optimization effectiveness, tackles the challenge of designing sparse multiple concentric elliptical arrays. By using the Sine Cosine Algorithm, the inter-element spacing and optimal determination of amplitude coefficients are achieved, resulting in a radiation pattern with reduced sidelobe level. To showcase the adaptability and superior performance of our proposed algorithm, we systematically evaluated its effectiveness across four distinct sets of concentric elliptical antenna arrays. These arrays comprise varying numbers of elements: (6, 12, 18 elements, 6, 12, 18, 24 elements, 6, 12, 18, 24, 30 elements and 6, 12, 18, 24, 30, 36 elements) with and without a central element. Using the sine cosine algorithm, we synthesize arrays and compare their sidelobe level and First Null Beam Width values with those from recent methods. Our findings consistently shows that the proposed algorithm consistently outperforms alternatives, yielding significantly lower sidelobe level values in all comparisons.

Efficient Aperture Fill Time Correction for Wideband Sparse Array Using Improved Variable Fractional Delay Filters.

To solve the problem of aperture fill time (AFT) for wideband sparse arrays, variable fractional delay (VFD) FIR filters are applied to eliminate linear coupling between spatial and time domains. However, the large dimensions of the filter coefficient matrix result in high system complexity. To alleviate the computational burden of solving VFD filter coefficients, a novel multi-regultion minimax (MRMM) model utilizing the sparse representation technique has been presented. The error function is constrained by the introduction of L2-norm and L1-norm regularizations within the minimax criterion. The L2-norm effectively resolves the problems of overfitting and non-unique solutions that arise in the sparse optimization of traditional minimax (MM) models. Meanwhile, the use of multiple L1-norms enables the optimal design of the smallest sub-filter number and order of the VFD filter. To solve the established nonconvex model, an improved sequential-alternating direction method of multipliers (S-ADMM) algorithm for filter coefficients is proposed, which utilizes sequential alternation to iteratively update multiple soft-thresholding problems. The experimental results show that the optimized VFD filter reduces system complexity significantly and corrects AFT effectively in a wideband sparse array.

Guided Ultrasonic Wave Monitoring of Damage in Anisotropic Composites

Lightweight, carbon fibre reinforced composites are often employed for the manufacture of aerospace components due to their good strength to weight ratio. However, due to poor interlaminar strength, composite components are prone to barely visible impact damage, caused by low velocity impacts, during aircraft operation. Guided-wave-based structural health monitoring (SHM) techniques, using a network of distributed sensors, is an important Structural Health Monitoring (SHM) tool for the detection and localization of in-service impact damage in composite structures. However, the material anisotropy of composite laminates influences guided wave propagation and scattering. These anisotropic effects, if unaccounted for, could lead to inaccurate localization of damage, and potential regions of the structure where guided waves cannot propagate with sufficient amplitude, reducing damage sensitivity. Defect characterization can be improved by considering the scattering characteristics of various damage types for the sparse array signal processing. Guided wave scattering (A0 Lamb wave mode) was investigated around an artificial insert delamination in a quasi-isotropic CFRP panel. Permanent magnets, mounted on an undamaged region of the plate, were also used as scattering targets and compared to the delamination case. Full 3D Finite Element (FE) simulations were performed for both the delamination and magnet cases and compared to wavefield data obtained from non-contact laser Doppler vibrometer (LDV) measurements. Individual ply layers were modelled using unidirectional composite material properties to accurately capture the anisotropy effects. Good agreement was found between the experimental measurements and simulations. Scattered guided wave amplitudes around each damage type show strong directional dependency with energy focusing along the fiber directions of the outer ply layers of the laminate. Distinct scattering behavior was observed for each damage type. Implications of anisotropy and angular scattering on sparse array SHM of different defect types are discussed.

Antenna Selection and Receive Beamforming for Multi-Functional Sparse Linear Array via Consensus ADMM

Antenna Selection and Receive Beamforming for Multi-Functional Sparse Linear Array via Consensus ADMM

Sparse microphone array design for frequency-invariant beamforming via group [formula omitted]-norm optimization

Frequency-invariant (FI) beamforming aims at recovering signals without any distortion and maintaining spatial selectivity over the entire bandwidth. However, most existing FI beamforming (FIB)methods consider the weighted ℓ1-norm or modified ℓ1-norm of the filters as the objective function for sparse FIB design, which cannot assure the optimal sparse solution. To deal with this drawback, an optimization problem for the FIB design of sparse microphone array in terms of ℓp-norm (0<p<1) minimization is formulated, where distortion-less response, mainlobe and sidelobe constraints are considered. Due to the existence of the ℓp-norm objective function, the resultant problem is nonconvex, and therefore, an alternating direction method of multipliers (ADMM) algorithm is devised. Specifically, the corresponding problem is decomposed into multi-block subproblems to determine all unknown variables separately. Then, the most dominant sensor positions for each frequency are determined by using the principal component analysis (PCA) and K-mean clustering algorithm. Numerical examples show that the proposed design achieves good directivity factor, frequency invariance and better sparsity.

High-order dilated nested arrays with increased degrees of freedom and reduced mutual coupling

In a companion paper [32], a sparse array called the dilated nested array (DNA) was introduced, which owns the same number of uniform degrees of freedom (DOFs) as the nested array but has the first and third constituent sub-arrays dense, since their sensors are uniformly spaced out by the critical inter-sensor spacing (2×λ/2). Therefore, in this paper we introduce two further dilations to address this shortcoming. In the first dilation - referred to as the one-sided dilated nested array (OS-DNA), Qf possible high-order extensions starting from the 2nd-order extension (Q=2) to the (Qf+1)th-order extension are applied to the third parent sub-array, and in the second dilation - referred to as the two-sided dilated nested array (TS-DNA), an additional extension is applied to the first parent sub-array. While the high-order extensions of the OS-DNA as well as the TS-DNA strictly preserve the same number of uniform DOFs of the parent DNA, the first extension of the OS-DNA eliminates all the sensor pairs with separation 2 in the third parent sub-array, whereas the Qth-order extensions (for 2<Q≤Qf+1) increase the number of nonuniform DOFs as Q increases. The TS-DNA then eliminates all the sensor pairs with spacing 2 in the first sub-array of the (Qf+1)th-order extension and concurrently maintains the higher number of nonuniform DOFs of this parent order. As such, the TS-DNA, named the super dilated nested array (SDNA) as well, still retains the number of uniform and nonuniform DOFs of the highest-order extension of the OS-DNA and at the same time enjoys the ideal critical weights of the co-prime array. Many theoretical properties are proved and extensive simulations are included to demonstrate the superior performance of these arrays.

Probabilistic impact localization in composites using wavelet scattering transform and multi-output Gaussian process regression

Data-driven machine-learning models offer considerable promise for acoustic source localization. However, many existing models rely on training data that correlates time-of-flight (TOF) measurements with source locations, yet they struggle to handle the complexities arising from nonlinear wave propagation in materials with varying properties. Furthermore, these models overlook the noise and uncertainties inherent in real-world experiments when predicting outputs. This paper aims to bridge a gap in impact localization for such structures, particularly focusing on scenarios involving noisy field measurements. This study proposes a framework based on probabilistic machine learning to identify impact locations, utilizing wavelet scattering transform (WST) and Multi-Output Gaussian Process Regression (moGPR). WST extracts informative features from Lamb waves, capturing relevant signatures for training the probabilistic machine learning model, while moGPR estimates correlated impact location coordinates (x, y) while accounting for inherent uncertainties in the data. To assess the proposed method’s performance in handling measurement uncertainties, an experiment was conducted using a CFRP composite panel instrumented with a sparse array of piezoelectric transducers. The results demonstrate that the probabilistic framework effectively addresses measurement uncertainties, enabling reliable source location estimation with confidence intervals and providing valuable insights for decision-making.

Development of a plastic scintillator based and SiPM readout detector for high-precision fast-timing measurements at rosphere array

We report on the development of a custom particle detection system that aims to improve the detection capabilities of the ROSPHERE array. The detector is designed specifically for lifetime measurements using the in-beam Fast-timing method for nuclear states in nuclides produced in nuclear transfer reactions. An 80 x 3.6 mm disc of BC400 plastic scintillator was coupled to a sparse SiPM array readout connected to a dedicated Front End Electronics board. The detector was tested versus a conical LaBr3(Ce) coupled to a Hamamatsu PMT readout. The timing measurements resulted in a resolution of 896(5) ps. The detector design, characteristics, and performances are presented in this paper along with future improvements.

TOBE-Costas arrays for fast high-resolution 3D power Doppler imaging.

2D sparse arrays and row-column arrays are both alternatives to 2D fully-addressed arrays with lower channel counts. Row-column arrays have recently demonstrated fast 3D structural and flow imaging but commonly suffer from high grating lobes or require multiplexing to achieve better quality. 2D sparse arrays enable full-volume acquisitions for each to transmit event, but plane-wave transmissions with them usually lack quality in terms of uniformity of wavefronts. Here, we propose a novel architecture that combines both types of these arrays in one aperture, enabling imaging using row-column or sparse arrays alone or a hybrid imaging scheme where the row-column array is used in transmission and a 2D sparse array in reception. This hybrid imaging scheme can potentially solve the shortcomings of each of these approaches. The sparse array layout chosen is a Costas array, characterized by having only one element per row and column, facilitating its integration with row-column (TOBE) arrays. We simulate images acquired with TOBE-Costas arrays using the hybrid imaging scheme and compare them to row-column and sparse spiral arrays of equivalent aperture size (128λ×128λ at 7.5 MHz) in ultrafast plane-wave imaging of point targets and 3D power Doppler imaging of synthetic flow phantoms. Our simulation results show that TOBE-Costas arrays exhibit superior resolution and lower side lobe levels compared to plane-wave compounding with row-column arrays. Compared to density-tapered spiral arrays, they provide a larger field of view and finer resolution.

Corrosion detection and localisation utilising a novel thin film guided wave sensor sparse array

Detecting and pinpointing corrosion-related damage in infrastructure poses significant financial and technical difficulties. It is simply not feasible to manually inspect all infrastructure for corrosion. Permanently installed systems can monitor critical failure points without the need for manual scanning but these are not always known. Ultrasonic guided wave technology can be used for full system coverage, however, there is a trade-off between range and resolution. This has found a place for long distance monitoring of significant defects, however high- resolution systems require too dense a network to be feasible to install, cost and maintain. This research seeks a middle ground between these two options by employing machine learning and a scalable setup of easy to install, printed detection sensors. A flexible array of printed thin film sensors is placed around two identical mild steel pipes, each 0.5m long with a 6 mm wall thickness and 115 mm outer diameter. Corrosion is then induced artificially in one sample. Two transmit transducers on the top and bottom of the pipes are used to emit an ultrasonic, circumferential guided wave. The transmitted waveform was then received on all sensors throughout the array for data collection. Through machine learning and a search algorithm, sub-millimetre resolution is achieved, detecting millimetre-scale corrosion and localisation to the nearest detection sensor. Crucially, the system maintains scalability for large-scale asset inspection. This technology shows promise in addressing inspection challenges, including Corrosion Under Pipe Supports (CUPS) and Corrosion Under Insulation (CUI). It offers a potential solution to the costly problem of infrastructure damage detection and localisation while retaining scalability and a low-profile design philosophy.

Analysis of Key Parameters of Spatial Power Combination of Sparse Array Time Reversal Signals

Based on the principle of spatial power combination of sparse array time reversed signals, the relationship of distances between constructive interference points to cross angle of beams and viewing angles has been analyzed. Then the formulas of distances between adjacent constructive points at 360-degree views are derived. Considering that the distance between constructive interference points around target is frequently needed to be less than target’s antenna size, The variation law of the view range with beam crossover angle has been simulated and analyzed with MATLAB when the above conditions are met at the same frequency and without errors. On this basis, array deployment recommendations are summed up. In addition, the effects of the received time window length and the difference of the distance between each array element and the target point on the synthesized power have been analyzed. Finally, the spatial power combination simulation experiments of the sparse array time-reversed signal have been carried out. The experimental results show that the methods and conclusions of this paper can provide theoretical basis and reference for the research and application of sparse array power combination technology in long-range wireless energy delivery, high-power microwave weapons, electronic warfare, etc.

DOA estimation technology based on array signal processing nested array

DOA estimation technology based on array signal processing nested array

Pushing the Limits of Acoustic Spatial Perception via Incident Angle Encoding

With the growing popularity of smart speakers, numerous novel acoustic sensing applications have been proposed for low-frequency human speech and high-frequency inaudible sounds. Spatial information plays a crucial role in these acoustic applications, enabling various location-based services. However, typically commercial microphone arrays face limitations in spatial perception of inaudible sounds due to their sparse array geometries optimized for low-frequency speech. In this paper, we introduce MetaAng, a system designed to augment microphone arrays by enabling wideband spatial perception across both speech signals and inaudible sounds by leveraging the spatial encoding capabilities of acoustic metasurfaces. Our design is grounded in the fact that, while sensitive to high-frequency signals, acoustic metasurfaces are almost non-responsive to low-frequency speech due to significant wavelength discrepancy. This observation allows us to integrate acoustic metasurfaces with sparse array geometry, simultaneously enhancing the spatial perception of high-frequency and low-frequency acoustic signals. To achieve this, we first utilize acoustic metasurfaces and a configuration optimization algorithm to encode the unique features for each incident angle. Then, we propose an unrolling soft thresholding network that employs neural-enhanced priors and compressive sensing for high-accuracy, high-resolution multi-source angle estimation. We implement a prototype, and experimental results demonstrate that MetaAng maintains robustness across various scenarios, facilitating multiple applications, including localization and tracking.

A semianalytically synthesized ultrathin photolithographic metagrating for sub-THz beam splitting

Compact and efficient devices for radiation beam manipulation are in increasing demand by terahertz technologies. Herein, we introduce an ultrathin metal-polymer photolithographic metagrating operating as a transmissive beam splitter with a wide refraction angle of 58∘ at sub-THz frequencies. Harnessing this recently proposed complex media platform, composed of sparse periodic arrays of meta-atoms, constraints of conventional metasurfaces with densely packed unit cells can be alleviated. The devised splitter, synthesized semianalytically and demonstrated experimentally, features deeply subwavelength thickness due to the unique manufacturing process employed, serving as a promising alternative to thick waveguide-based metagratings previously reported for this frequency range.

128-channel optical phased array with large field of view and low main-lobe attenuation

In this paper, a 128-channel non-uniform optical phased array is proposed. The antenna is based on a silicon nitride waveguide with a large cross-sectional area (3 μm × 1.2 μm) and a silicon nitride grating with a small diffraction window (grating width of 200 nm), enabling high optical power transmission and a wide 1 dB field of view. As a result, the designed sparse optical phased array achieves less than 1 dB of main lobe attenuation over a 94° field of view. Within this field of view, the main lobe will not fall below 80% of the maximum main lobe. This allows the minimum detection distance to still be about 89% of the maximum detection distance without increasing the input power. In this field of view, the maximum side-lobe suppression of the designed sparse optical phased array is 13.4 dB, and the minimum side-lobe suppression is higher than 11.9 dB. This is useful for simultaneously achieving a large field of view, low main lobe attenuation, stable side-lobe suppression, and long detection distance.

An efficient position optimization method based on improved genetic algorithm and machine learning for sparse array

Sparse array antennas are significant in communication and radar systems due to the advantages of high resolution, low complexity, and immunity to interference. In this letter, a novel array synthesis method based on an improved genetic algorithm (IGA) is proposed to optimize the antenna position in the sparse array. Different from the traditional generic methods with high computational complexity, a method combined with Gaussian process regression (GPR) prediction is proposed to optimize the antenna position with low computational complexity. The proposed method can minimize the peak sidelobe level (PSLL) during the beam scanning procedures. Moreover, the minimum antenna spacing constraint is also considered to ensure a physically achievable solution. Simulation results show that the proposed method can achieve a lower PSLL with fewer iterations and provide a global optimization solution for large array synthesis compared with existing methods. Furthermore, a prototype of the sparse array principle optimized by IGA is designed. Measurement results indicate that the optimized sparse array has good synthesis performance in high frequency.

Low sidelobe planar electrically large sparse array antenna with element number reduction based on genetic algorithm

AbstractConventionally, both electrically larger (EL) arrays and sparse arrays offer the advantage of element number reduction but disadvantage of high sidelobe levels. A new scheme of planar EL sparse array antenna based on a genetic algorithm (GA) to achieve low sidelobe with element number reduction is proposed. To begin with, EL sparse array antenna optimisation models based on GA for both linear and planar arrays are analysed. Then, an EL slot antenna element based on a 3 × 3 substrate integrated waveguide cavity is designed. An 8‐element linear EL sparse array antenna is designed and compared with a uniform array antenna, demonstrating a reduction in the maximum sidelobe level (MSLL) by nearly 4.6 dB. After that, a 4 × 8 element planar EL sparse array antenna is fabricated and measured. Compared to an 8 × 16 element planar EL uniform array antenna, the number of antenna elements is reduced by 75%, while the MSLL is reduced by approximately 3 dB. The measured −10 dB impedance bandwidth ranges from 25.3 to 27.8 GHz. At the central frequency, the radiation pattern achieves a peak gain of 29.6 dBi, exhibiting low sidelobe levels below −15.0 dB.

Efficient gridless 2D DOA estimation based on generalized matrix‐form atomic norm minimization

AbstractTwo‐dimensional (2D) direction‐of‐arrival (DOA) estimation is crucial in array signal processing. Compressed sensing (CS) provides a superior alternative to spatial spectrum estimation algorithms by enabling 2D DOA estimation of correlated sources from single snapshot data. However, the grid mismatch effect inherent in grid‐based CS algorithms impacts estimation accuracy. Despite recent advancements, the state‐of‐the‐art gridless CS algorithm, decoupled atomic norm minimization, is limited to specific 2D array geometries, such as uniform rectangular arrays. This letter presents an efficient gridless 2D DOA estimation algorithm for generalized rectangular arrays, including both uniform and sparse arrays. The proposed algorithm achieves high accuracy through a novel approach called generalized matrix‐form atomic norm minimization and provides a fast solution using the alternating direction method of multipliers. Validation through computer simulations and practical experiments underscores its efficacy.

High-accuracy 2D DOA estimation with three parallel sparse nested array

To improve the performance for two-dimensional (2D) direction of arrival (DOA) estimation in the presence of limited antennas in practice, we propose a novel three parallel sparse nested array with extended aperture and reduced mutual coupling. The proposed array consists of three subarrays where locations of antennas have closed-form expressions, to efficiently achieve increased degrees of freedom in difference coarray domain. The unit inter-antenna spacings and the displacements among subarrays are all expanded by D1, D2 or D3 fold for aperture extension and mutual coupling reduction in physical array domain. By exploiting the configuration, the two methods, i.e., BOMP-based and CMT-BCS-based methods, are further explored for high-precision but ambiguous angle estimation, and then the coprime-ness between the displacements is utilized to solve the angle ambiguities. Simulation results demonstrate the superiority of the proposed array and methods for 2D DOA estimation over the existing technologies.

Reduced-complexity multiple parameters estimation via toeplitz matrix triple iteration reconstruction with bistatic MIMO radar

In this advanced exploration, we focus on multiple parameters estimation in bistatic Multiple-Input Multiple-Output (MIMO) radar systems, a crucial technique for target localization and imaging. Our research innovatively addresses the joint estimation of the Direction of Departure (DOD), Direction of Arrival (DOA), and Doppler frequency for incoherent targets. We propose a novel approach that significantly reduces computational complexity by utilizing the Temporal-Spatial Nested Sampling Model (TSNSM). Our methodology begins with a multi-linear mapping mechanism to efficiently eliminate unnecessary virtual Degrees of Freedom (DOFs) and reorganize the remaining ones. We then employ the Toeplitz matrix triple iteration reconstruction method, surpassing the traditional Temporal-Spatial Smoothing Window (TSSW) approach, to mitigate the single snapshot effect and reduce computational demands. We further refine the high-dimensional ESPRIT algorithm for joint estimation of DOD, DOA, and Doppler frequency, eliminating the need for additional parameter pairing. Moreover, we meticulously derive the Cramér-Rao Bound (CRB) for the TSNSM. This signal model allows for a second expansion of DOFs in time and space domains, achieving high precision in target angle and Doppler frequency estimation with low computational complexity. Our adaptable algorithm is validated through simulations and is suitable for sparse array MIMO radars with various structures, ensuring higher precision in parameter estimation with less complexity burden.