Explore the vast range of titles available.

The total focusing method (TFM) is often considered to be the ‘gold standard’ for ultrasonic imaging in the field of nondestructive testing. The use of matrix phased arrays as probes allows for high-resolution volumetric TFM imaging. Conventional TFM imaging involves the use of full matrix capture (FMC) for ultrasonic signals acquisition, but in the case of a matrix phased array, this approach is associated with a huge volume of data to be acquired and processed. This severely limits the frame rate of volumetric imaging with 2D probes and necessitates the use of high-end equipment. Thus, the aim of this research was to develop a novel design method for determining the optimal sparse 2D probe configuration for specific conditions of ultrasonic imaging. The developed approach is based on simulated annealing and involves implementing the solution of the sparse matrix phased array layout optimization problem. In order to implement simulated annealing for the aforementioned task, its parameters were set, the acceptance function was introduced, and the approaches were proposed to compute beam directivity diagrams of sparse matrix phased arrays in TFM imaging. Experimental studies have shown that the proposed approach provides high-quality volumetric imaging with a decrease in data volume of up to 84% compared to that obtained using the FMC data acquisition method.

The synthesis problem of the number of array elements, array element spacing, and array formation is widely concerned in sparse array optimization. The local optimum problem is still an urgent problem to be solved in the existing optimization algorithms. A sparse array optimization algorithm on improved sparrow search algorithm (ISSA) is proposed in this paper. Firstly, a probabilistic following strategy is proposed to optimize the following strategy of the sparrow search algorithm (SSA), and it can improve the global search capability of the algorithm. Secondly, the adaptive local search and Cauchy–Gaussian mutation are used to avoid falling into the local optimum situation, and more high‐quality areas are searched to improve the local extremum escape ability and convergence performance of the algorithm. Finally, the peak sidelobe level (PSLL) is used as the fitness function to adaptively optimize the position of the array elements. Experimental simulations show that the proposed approach has good main lobe response and low sidelobe response. In the sparse planar array, the sidelobe level decreases by −1.41 dB compared with the genetic algorithm (GA) and 0.69 dB lower than the SSA. In sparse linear array, the sidelobe level decreases by −1.09 dB compared with differential evolution algorithm and 0.40 dB lower than the SSA. The optimization of sparse arrays significantly enhances the accuracy and robustness of antenna array error estimation.

Compressed sensing (CS) has been successfully applied to the synthesis of maximally sparse non-uniform linear array with the synthesised pattern matching the reference pattern very well by using as few elements as possible. According to the CS theory, a sparse or compressible high-dimensional signal can be first projected onto a low-dimensional space through a measurement matrix, and then recovered accurately by using a variety of practical algorithms based on the low-dimensional information. The proposed approach can synthesise the sparse linear arrays fitting the desired patterns with a minimum number of elements. Numerical simulations validate the effectiveness and advantages of the proposed synthesis method. Moreover, compared with the existing sparse-array synthesis methods, the author's method is more robust and accurate, while maintaining the advantage of easy implementation.

Sparse arrays can increase the array aperture, thereby enhancing angular resolution. However, this also introduces additional computational complexity. This letter proposes a symmetric sparse array structure, where subarrays with different inter‐element spacings sample distinct spatial domain signals, analogous to the use of mother wavelets at different scales in wavelet theory to process various frequency components of a signal. The root‐MUSIC method can be directly applied to the proposed method, and the simulations demonstrate that it achieves direction‐of‐arrival estimation performance comparable to that of super‐nested arrays while maintaining lower computational complexity.

Sparse arrays have grating lobes in the far field pattern due to the large spacing of elements residing in a rectangular or triangular grid. Random element spacing removes the grating lobes but produces large variations in element density across the aperture. In fact, some areas are so dense that the elements overlap. This paper introduces a low discrepancy sequence (LDS) for generating the element locations in sparse planar arrays without grating lobes. This nonrandom alternative finds an element layout that reduces the grating lobes while keeping the elements far enough apart for practical construction. Our studies consider uniform sparse LDS arrays with 86% less elements than a fully populated array, and numerical results are presented that show these sampling techniques are capable of completely removing the grating lobes of sparse arrays. We present the mathematical formulation for implementing an LDS generated element lattice for sparse planar arrays, and present numerical results on their performance. Multiple array configurations are studied, and we show that these LDS techniques are not impacted by the type/shape of the planar array. Moreover, in comparison between the LDS techniques, we show that the Poisson disk sampling technique outperforms all other approaches and is the recommended LDS technique for sparse arrays.

This paper proposes a novel sparse array design and an efficient algorithm for two-dimensional direction-of-arrival (2D-DOA) estimation. By analyzing the hole distribution in coprime arrays and introducing supplementary elements, we design a Complementary Coprime Planar Array (CCPA) that strategically fills key holes in the virtual array. This design enhances the array’s continuous Degrees Of Freedom (DOFs) and virtual aperture, achieving improved performance in 2D-DOA estimation with fewer physical elements. The virtualization of the array further increases the available DOFs, while the hole-filling strategy ensures better spatial coverage and continuity. On the algorithmic side, we introduce a dimensionality-reduction root MUSIC algorithm tailored for uniform planar arrays after virtualization. By decomposing the two-dimensional spectral peak search into two one-dimensional polynomial root-finding problems, the proposed method significantly reduces computational complexity while maintaining high estimation accuracy. This approach effectively mitigates the challenges of 2D peak search, making it computationally efficient without sacrificing precision. Extensive simulations demonstrate the advantages of the proposed array and algorithm, including higher DOFs, reduced complexity, and superior estimation performance compared to existing methods. These results validate the effectiveness of the proposed framework in advancing sparse array design and signal processing for 2D-DOA estimation.

The total focusing method (TFM) implemented in time domain has high complexity, limiting its large-scale engineering applications. In contrast, the frequency-domain TFM based on phase shift migration (PSM) is helpful to improve imaging efficiency. On this basis, partial data can be abandoned for imaging to save computing costs and shorten processing time without reducing imaging quality. In this paper, an adaptive sparse-TFM in the frequency domain is proposed by selecting desired signals from full matrix capture (FMC) data. The amplitude-squared parameter for all the received signals corresponding to each transmitting element is taken as the relevant weight, and sparse arrays are constructed adaptively according to the descending order of numerical values. Simulated and experimental results indicate that the proposed method has stable imaging quality under different sparsity ratios, rapidly improving imaging efficiency with good resolution for defect detection.

Recently, the concept of the difference and sum co-array (DSCa) has attracted much attention in array signal processing due to its high degree of freedom (DOF). In this paper, the DSCa of the nested array (NA) is analyzed and then an improved nested configuration known as the diff-sum nested array (DsNA) is proposed. We find and prove that the sum set for the NA contains all the elements in the difference set. Thus, there exists the dual characteristic between the two sets, i.e., for the difference result between any two sensor locations of the NA, one equivalent non-negative/non-positive sum result of two other sensor locations can always be found. In order to reduce the redundancy for further DOF enhancement, we develop a new DsNA configuration by moving nearly half the dense sensors of the NA to the right side of the sparse uniform linear array (ULA) part. These moved sensors together with the original sparse ULA form an extended sparse ULA. For analysis, we provide the closed form expressions of the DsNA locations as well as the DOF. Compared with some novel sparse arrays with large aperture such as the NA, coprime array and augmented nested array, the DsNA can achieve a higher number of DOF. The effectiveness of the proposed array is proved by the simulations.

This study aims to illustrate the main aspects of designing a modular 120 GHz Multiple-Input Multiple-Output (MIMO) sparse radar array (SRA) composed of multiple Single-Input Single-Output (SISO) Integrated Circuits (ICs). Although the scientific literature reports on 120 GHz integrated circuit prototypes, to the authors’ best knowledge, there are no commercial MIMO radars composed of multiple SISO ICs operating in the D-band spectrum. The design involves many challenges; indeed, the necessity to combine multiple chips with fixed dimensions and the presence of transmitting and receiving antennas on chips add many constraints for the antenna placement and, consequently, for the virtual array design. As an example, the minimum distance between the antennas must be at least equal to the chip width, which is in turn higher than half a wavelength and renders the array into a sparse configuration, thus raising many concerns regarding fixing the optimum inter-chip distance. Thus, this contribution can be considered as pioneering, being focused on the emerging concept of designing D-band MIMO radars by exploiting a modular approach.

To address the weakness that the difference co-array (DCA) only enhances the degrees of freedom (DOFs) to a limited extent, a new configuration called the generalized nested array via difference–sum co-array (GNA-DSCA) is proposed for direction of arrival (DOA) estimation. We consider both the temporal and spatial information of the array output to construct the DSCA model, based on which the DCA and sum co-array (SCA) of the GNA are systematically analyzed. The closed-form expression of the DOFs for the GNA-DSCA is derived under the determined dilation factors. The optimal results show that the GNA-DSCA has a more flexible configuration and more DOFs than the GNA-DCA. Moreover, the larger dilation factors yield significantly wider virtual aperture, which indicates that it is more attractive than the reported DSCA-based sparse arrays. Finally, a hole-filling strategy based on atomic norm minimization (ANM) is utilized to overcome the degradation of the estimation performance due to the non-uniform virtual array, thus achieving accurate DOA estimation. The simulation results verify the superiority of the proposed configuration in terms of virtual array properties and estimation performance.