Search Results (124)

A circular array transducer with high frequency and small aperture size is highly desired for intravascular ultrasound (IVUS) imaging application. With the breakthrough of array transducer techniques, high-frequency circular array transducers with the advantages of high frame rate and high resolution have been developed and manufactured. Focusing on the development of a matched high-frequency imaging algorithms, this study introduces apodization functions into 55 MHz circular-array IVUS imaging, proposes a circular-array-specific apodization model, and breaks the lateral-resolution limit inherent to conventional delay-and-sum (DAS) beamforming. In the study, three typical algorithms—synthetic aperture (SA), apodized synthetic aperture (ASA), and sparse synthetic aperture (SSA)—are investigated in order to well achieve an effective imaging result for our newly derived circular array transducer with 55 MHz. In the scatterer’s simulation, at a depth of 1.5 mm, the ASA algorithm improves the lateral resolution from 260 μm for conventional SA to 175 μm (a 33% enhancement), while tripling the frame rate. Meanwhile, SSA maintains a resolution of 300 μm and reduces the data volume by 50%, laying the groundwork for real-time 3D imaging. Further phantom imaging testing shows that the SA algorithm has the best imaging effect on regional defects. The ASA algorithm has the best imaging effect on point defects while improving the imaging frame rate. This study provides insights and a foundation for optimizing circular-array intravascular ultrasound imaging, the proposed ASA model can be directly ported to existing 40–60 MHz circular-array IVUS systems, offering a new route for accurate early-plaque identification. Full article

Directional Antenna Modulation (DAM) utilizing 2D conformal phased arrays has been demonstrated to enable secure Multi-directional Independent Transmission (MIT) over a broad angular range. This paper proposes an unbalanced DAM technique that dynamically allocates power according to transmission distance, thereby significantly enhancing transmission efficiency in practical scenarios where receivers are located at varying distances. In particular, a high-efficiency Differential Evolution (DE) optimization algorithm integrated with an “alien species invasion” mechanism is developed to accelerate convergence and optimize the phase delays of each array element. Bit Error Rate (BER) analysis for MIT reveals superior directional security compared to traditional methods, with conformal arrays providing wider angular coverage and spherical sparse arrays overcoming the half-wavelength spacing limitation. The simulation results validate that the proposed system achieves simultaneous secure transmissions in multiple directions while maintaining a BER below −40 dB. Full article

Passive acoustic sensing with arrays has applications in many fields, including atmospheric monitoring of low frequency sounds (i.e., infrasound). Beamforming of array signals to gain spatial information about the signal is common, but the performance is often degraded due to limited resources (e.g., number of sensors, array size). Such sparse arrays create ambiguities due to reduced resolution and spatial aliasing. While previous work has focused on either maximizing array resolution or minimizing spatial aliasing, the current study demonstrates how evolutionary algorithms can be utilized to identify array configurations that optimize for both properties. The non-dominated sorting genetic algorithm II (NSGA-II) was used with the beamwidth and maximum sidelobe level as the fitness functions to iteratively identify a group of optimized synthesized array configurations. This group is termed a Pareto-front and is optimized such that one fitness function cannot be improved without a decrease in the other. These optimized solutions were studied for a single frequency (8 Hz) and a multi-frequency (3 to 20 Hz) signal using either a 36-element or 9-element array with a 60 m aperture. The performance of the synthesized arrays was compared against established array configurations (baseline) with most of the Pareto-front solutions outperforming these baseline configurations. The largest improvements to array performance using the synthesized configurations were with fewer array elements and the multi-frequency signal. Full article

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a nonlinear relationship. In this paper, employing a symmetric enhanced nested array, a high-precision underdetermined three-dimensional (3D) NF localization method is proposed. Firstly, the symmetry of the array and the fourth-order cumulant are utilized to construct the equivalent virtual far-field (FF) received data. Then, a gridless, sparse, and parametric approach combined with an l₁-singular value decomposition-based pairing procedure is employed to obtain estimates of two paired angles. Finally, a one-dimensional (1D) spectral estimator is applied to obtain the estimate of the range parameter. By analyzing the virtual aperture, the optimal parameter configuration for a given number of elements is obtained. As shown by simulation results, the proposed method can handle underdetermined estimation. Compared with the other algorithms, the proposed algorithm achieves significant improvements in both angular and distance accuracy, with enhancements of 65% and 61.7%, respectively. Full article

This study proposes a hybrid optimization algorithm (IFWA-SA) integrating an improved fireworks algorithm with simulated annealing for sparse array synthesis in multiple-input multiple-output (MIMO) radar systems. The innovation lies in synergistically combining the multidimensional directional explosion mechanism of the fireworks algorithm for global exploration with simulated annealing’s probabilistic jumping strategy for local optimization. Initial populations generated via Sobol sequences eliminate local clustering from random initialization. During global exploration, the proposed discrete variant of the fireworks algorithm, tailored for sparse array optimization, significantly enhances the search efficiency, while temperature-controlled probabilistic optimization refines array aperture and element spacing to escape local optima during local refinement. Comparative experiments with particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA) and gray wolf optimization (GWO) demonstrated that the proposed method effectively suppresses sidelobes. On average, the IFWA-SA reduced the peak sidelobe level (PSL) by about 1.3–3.8 dB compared with the benchmark algorithms, confirming its superior convergence capability and effectiveness in synthesizing high-performance sparse arrays. Full article

A sparse antenna array of subarrays can effectively reduce the digital channels of array antennas, system complexities, and hardware cost while simultaneously increasing the antenna aperture. In this study, a new optimal design is proposed for a sparse antenna array of subarrays in the full-phased multiple input multiple output (FPMIMO) operation mode based on genetic algorithm–particle swarm optimization (GA–PSO) and ambiguity functions. The proposed algorithm can adaptively adjust the number of optimization iterations for yielding the optimization results of the PSO algorithm and GA to ensure the global optimization performance of algorithms and combine ambiguity functions to determine the final optimized sparse antenna array of subarrays. The effectiveness of the proposed algorithm is verified via simulation tests. Full article

The challenge of achieving high-precision direction-of-arrival (DOA) estimation with enhanced degrees of freedom (DOFs) under a limited number of physical array elements remains a critical issue in array signal processing. To address this limitation, this paper makes the following three key contributions: (1) a novel moving sparse array synthesis model incorporating time-frequency-spatial joint processing for coprime frequencies signal sources; (2) an optimized coprime frequencies-based unmanned aerial vehicle array (CF-UAVA) configuration with derived closed-form expressions for the distribution of synthesized array; and (3) two DOA estimation methods: a group sparsity-based approach universally applicable to the proposed aperture synthesis model and a joint group sparsity and virtual array interpolation tailored for the proposed CF-UAVA configuration. Comprehensive simulation results demonstrate the superior DOA estimation accuracy and increased DOFs achieved by our proposed aperture synthesis model and DOA estimation algorithms compared to conventional approaches. Full article

Mass spectrometric (MS) fingerprinting coupled with chemometrics for the detection of plants in plant mixtures is sparsely researched. This paper aims to check its value for herbal adulteration concerning plants with slimming as an indication. Moreover, it is among the first to exploit the full three-dimensional dataset (i.e., time × intensity × mass) obtained with liquid chromatography hyphenated with MS for herbal fingerprinting purposes. The MS parameters were optimized to achieve highly specific fingerprints. Trituration’s (total 55), blanks (total 11) and reference plants were injected in the MS system to generate the dataset. The dataset was complex and humongous, necessitating the application of compression techniques. After compression, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to generate models validated for accuracy using cross-validation and an external test set. Confusion matrices were constructed to provide insight into the modeling predictions. A complimentary evaluation between data obtained using a previously developed Diode Array Detection (DAD) method and the MS data was performed by data fusion techniques and newly generated models. The fused dataset models were comparable to MS models. For ease of application, MS modeling was deemed to be superior. The future market studies would adopt MS modeling as the preferred choice. A proof of concept was carried out on 10 real-life samples obtained from illegal sources. The results indicated the need for stronger monitoring of (illegal) plant food supplements entering the market, especially via the internet. Full article

This study presents a joint optimization framework for radar positions and subpulse carrier frequencies to address mainlobe jamming suppression in a distributed array radar system with one main and multiple auxiliary radars. Accounting for gain and aperture differences between the main and auxiliary radars, the grating lobe effect on jamming suppression performance is analyzed. Unlike conventional sparse array design approaches, this work introduces an architecture leveraging subpulses at distinct carrier frequencies to enhance grating lobe suppression and jamming suppression. A specific joint optimization method for radar positions and subpulse frequencies is then established. With jamming suppression performance as the objective function, the method first maps the variations induced by a range of candidate frequencies onto a single representative frequency point. This mapping enables efficient optimization of radar positions across the designated frequency band. Subsequently, a sequential scheme selects specific carrier frequencies for the subpulses. In practical anti-jamming operations, the optimal frequency for the current scenario is determined by analyzing the suppression results from these subpulses. The main radar then transmits pulses at this optimal frequency, thereby reducing both system complexity and pulse accumulation difficulty. Simulation results demonstrate that the proposed method achieves a reduction of over 3 dB in grating lobe suppression compared to conventional sparse array design methods, while enhancing the output signal-to-jamming and noise ratio by nearly 3 dB after jamming suppression. Full article

Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains challenging due to issues such as noise contamination and incomplete echo data. By introducing sparse priors such as $L_1$ regularization functions, image quality can be improved to a certain extent and the impact of noise can be reduced. However, in scenarios involving distributed targets, the aforementioned methods often fail to maintain continuous structural features such as edges and contours, thereby limiting their reconstruction performance and adaptability. Recent studies have introduced geometric regularization functions to preserve the structural continuity of targets, yet these lack multi-prior consensus, resulting in limited reconstruction quality and robustness in complex scenarios. To address the above issues, a novel array SAR 3D reconstruction method based on multi-prior collaboration (ASAR-MPC) is proposed in this article. In this method, firstly, each optimization module in 3D reconstruction based on multi-prior is treated as an independent function module, and these modules are reformulated as parallel operations rather than sequential utilization. During the reconstruction process, the solution is constrained within the solution space of the module, ensuring that the SAR image simultaneously satisfies multiple prior conditions and achieves a coordinated balance among different priors. Then, a collaborative equilibrium framework based on Mann iteration is presented to solve the optimization problem of 3D reconstruction, which can ensure convergence to an equilibrium point and achieve the joint optimization of all modules. Finally, a series of simulation and experimental tests are described to validate the proposed method. The experimental results show that under limited echo and noise conditions, the proposed method outperforms existing methods in reconstructing complex target structures. Full article

To comprehensively address the challenges of aperture design, element spacing optimization, and sidelobe suppression in sparse radar array antennas, this paper proposes a hybrid particle swarm optimization (PSO) algorithm that integrates quantum-behavior mechanisms with genetic mutation. The algorithm enhances global search capability through the introduction of a quantum potential well model, while incorporating adaptive mutation operations to prevent premature convergence, thereby improving optimization accuracy during later iterations. The simulation results demonstrate that for sparse linear arrays, planar rectangular arrays, and multi-ring concentric circular arrays, the proposed algorithm achieves a sidelobe level (SLL) reduction exceeding 0.24 dB compared to conventional approaches, including the grey wolf optimizer (GWO), the whale optimization algorithm (WOA), and classical PSO. Furthermore, it exhibits superior global iterative search performance and demonstrates broader applicability across various array configurations. Full article

Existing array calibration methods rely on the geometric characteristics of the array or signal matrix, which limits their flexibility and robustness. This study addresses this challenge by proposing a novel joint sparse estimation method for array gain and phase calibration. By leveraging the sparsity of calibration signals and the dictionary mismatch model, the proposed method, based on the fast iterative shrinkage-thresholding algorithm (FISTA), jointly estimates the discrete on-grid azimuths and continuous off-grid offsets of the direction of arrival (DOA) of calibration signals. The method employs a spatial domain filtering technique based on the maximum a posteriori probability to mitigate the bias induced by phase errors in the calibration signal estimation, enhancing calibration accuracy. Furthermore, the iterative estimation framework was optimized to extend the applicability of the method from linear to uniform planar arrays. The results demonstrated that the root mean squared error (RMSE) of the beam pattern for various array types decreased by one to two orders of magnitude after calibration. Compared with existing state-of-the-art methods, the proposed approach exhibited stable performance and superior estimation accuracy under conventional signal-to-noise ratio conditions. Moreover, the proposed method maintained high stability and lower RMSE as the gain and phase error values increased. Full article

In this study, a new sparse reconstruction algorithm based on near-field imaging is proposed to solve the grating lobe problem arising from sparse arrays. It derives the expression of the steering vector under near-field conditions and formulates the optimization problem. Moreover, the problem, by using the alternating direction method of multipliers (ADMM), is efficiently solved. Experiment results indicate that, in comparison to existing methods such as the phase coherent factor (PCF) and sum and difference beams, the proposed method significantly reduces the impact caused by grating lobes in near-field imaging. In addition, the proposed algorithm is accelerated by the ADMM, which significantly reduces the computational time. This work offers a perspective and potential solution to enhance grating lobe images. Full article

Traditionally, microphone array networks for 3D sound source localization rely on centralized data processing, which can limit scalability and robustness. In this article, we recast the task of sound source localization (SSL) with networks of acoustic arrays as a distributed optimization problem. We then present two resolution approaches of such a problem; one is computationally centralized, while the other is computationally distributed and based on an Adapt-Then-Combine (ATC) diffusion strategy. In particular, we address 3D SSL with a network of linear microphone arrays, each of which estimates a stream of 2D directions of arrival (DoAs) and they cooperate with each other to localize a single sound source. We develop adaptive cooperation strategies to penalize the arrays with the most detrimental effects on localization accuracy and improve performance through error-based and distance-based penalties. The performance of the method is evaluated using increasingly complex DoA stream models and simulated acoustic environments characterized by various levels of reverberation and signal-to-noise ratio (SNR). Furthermore, we investigate how the performance is related to the connectivity of the network and show that the proposed approach maintains high localization accuracy and stability even in sparsely connected networks. Full article

In many river basins worldwide, decision-making depends on limited data and information. Yet, decisions, like the planning of a new multi-purpose dam, must be taken relying on available data. The incorporation of socio-economic developments, climate or land use changes into this process remains a separate concern. Undoubtedly, authorities worldwide possess undisclosed data, which complicates scientific efforts. This study aims to address the challenges of developing a hydrological and water management model for the data-scarce and extensively managed Volta River Basin in West Africa. To overcome the limitations posed by sparse easily accessible observational data, a time- and resource-demanding data integration approach was applied using a diverse array of data sources covering various time periods, including manually digitized analog records from hydrological yearbooks, graphics, and other multilingual sources. This approach has been shown to enhance the spatio-temporal availability of data, thereby allowing for the optimization of model parameters to simulate the increasing impact of human intervention on river discharge. The incorporation of comprehensive data has enhanced the robustness of the model, where complex hydrological processes and water management dynamics are captured with greater accuracy. This would not have been possible if only the easily accessible data had been used. Full article