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The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

The purpose of this paper is to develop a new method for the two‐dimensional direction‐of‐arrival (DOA) estimation of multiple coherently distributed (CD) sources, which can provide lower computational complexity while sustaining the estimation performance within a tolerable level.

Using three parallel uniform linear arrays (ULAs), a new method for parametric estimation of multiple coherently distributed sources is proposed. The proposed method is based on the Taylor approximation to the generalized steering vectors (GSVs) of shifted ULAs, and utilizes the special array geometry. In addition, a simple parameter matching procedure is also given in this paper.

Several numerical experiments have been designed. The experiments are based on coherently distributed source model, and the noise is assumed to be zero‐mean and spatially and temporally white and Gaussian. Numerical results show that the proposed method can exhibit good estimation performance under small angular spread and be applicable to the multisource scenario with different angular distributions.

This research is limited to CD sources. Furthermore, the proposed method is based on the small angular approximation to GSV. Hence, it is fitter for the case of small angular extension.

Without any spectrum‐peak searching, the proposed method provides lower computational cost compared to the classical spectrum‐based methods. Moreover, it does not depend on the prior knowledge about angular distribution shape and is hence robust to mismodeling.

The purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).

The relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.

Variations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.

DAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

Optimization involves changing the input parameters of a process that is experimented with different conditions to obtain the maximum or minimum result. Increasing interest is shown by antenna researchers in finding the optimum solution for designing complex antenna arrays which are possible by optimization techniques.

Design of antenna array is a significant electro-magnetic problem of optimization in the current era. The philosophy of optimization is to find the best solution among several available alternatives. In an antenna array, energy is wasted due to side lobe levels which can be reduced by various optimization techniques. Currently, developing optimization techniques applicable for various types of antenna arrays is focused on by researchers.

In the paper, different optimization algorithms for reducing the side lobe level of the antenna array are presented. Specifically, genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), cuckoo search algorithm (CSA), invasive weed optimization (IWO), whale optimization algorithm (WOA), fruitfly optimization algorithm (FOA), firefly algorithm (FA), cat swarm optimization (CSO), dragonfly algorithm (DA), enhanced firefly algorithm (EFA) and bat flower pollinator (BFP) are the most popular optimization techniques. Various metrics such as gain enhancement, reduction of side lobe, speed of convergence and the directivity of these algorithms are discussed. Faster convergence is provided by the GA which is used for genetic operator randomization. GA provides improved efficiency of computation with the extreme optimal result as well as outperforming other algorithms of optimization in finding the best solution.

The originality of the paper includes a study that reveals the usage of the different antennas and their importance in various applications.

Direction-of-arrival (DOA) estimation for wideband sources has attracted a growing interest in the recent decade because wideband sources are incorporated in many real-world applications such as communication systems, radar, sonar and acoustics. One way to estimate the DOAs of wideband signals is to decompose it into narrowband signals using discrete Fourier transform (DFT) and then apply well-established narrowband algorithms to each signal. Afterwards, results are averaged to yield the final DOAs. These techniques require scanning the full band of wideband sources, ultimately degrading the resolution and increasing complexity. This paper aims to propose a new DOA estimation methodology to solve these problems.

The new DOA estimation methodology is based on incoherent signal subspace method (ISSM). The proposed approach presents a criterion to select a single sub-band of the selected narrowband signals instead of scanning the whole signal spectrum. Then, the DOAs of wideband signals are estimated using the selected sub-band. Therefore, it is named as single sub-band (SSB)-ISSM.

The computational complexity of the proposed method is much lower than that of traditional DFT-based methods. The effectiveness and advantages of the proposed methodology are theoretically investigated, and computational complexity is also addressed.

To verify the theoretical analysis, computer simulations are implemented, and comparisons with other algorithms are made. The simulation results show that the proposed method achieves better performance and accurately estimates the DOAs of wideband sources.

The existing dimensionality reduction algorithms suffer serious performance degradation under low signal-to-noise ratio (SNR) owing to the presence of noise. To address these problems, an enhanced spatial smoothing scheme is proposed that exploits the subarray time-space correlation matrices to reconstruct the data matrix to overcome this weakness. This method uses the strong correlation of signal and the weak correlation of noise in time and space domains, which improves the noise suppression ability.

In this paper, an enhanced spatial smoothing method is proposed. By using the strong correlation of signal and the weak correlation of noise, the time-space smoothed array covariance matrix based on the subarray time-space correlation matrices is constructed to improve the noise suppression ability. Compared with the existing Toeplitz matrix reconstruction and spatial smoothing methods, the proposed method improves the DOA estimation performance at low SNR.

Theoretical analysis and simulation results show that compared with the existing dimensionality reduction processing algorithms, the proposed method improves the DOA estimation performance in cases with a low SNR. Furthermore, in cases where the DOAs between the coherent sources are closely spaced and the snapshot number is low, our proposed method significantly improves the performance of the DOA estimation performance.

The proposed method improves the DOA estimation performance at low SNR. In particular, for the cases with a low SNR, the proposed method provides a better RMSE. The convergence of the proposed method is also faster than other methods for the low number of snapshots. Our analysis also confirms that in cases where the DOAs between the coherent sources are closely spaced, the proposed method achieves a much higher angular resolution than that of the other methods.

The purpose of this paper is to improve active sonar detection performance in shallow water. A stochastic‐like model multivariate elliptically contoured (MEC) distributions is defined to model reverberation, which helps to reveal structure information of target signatures.

Active sonar systems have been developed with wider transmission bandwidths and larger aperture receiving array, which improve the signal‐to‐noise ratio and reverberation power ratio after matched filtering and beamforming. But, it has changed the statistical distribution of the reverberation‐induced envelope from the traditionally assumed Rayleigh distribution. The MEC is a kind of generalized non‐Gaussian distribution model. The authors theoretically derive the compound Gaussian, Rayleigh‐mixture, Weibull, K distributions are all special cases of MEC. It is known that Weibull and K distributions have obvious heavy‐tail than Rayleigh distribution. MEC is a suitable model to characterize non‐Rayleigh heavy‐tailed distribution of reverberation.

The analysis of test data shows reverberation envelopes obviously deviate Rayleigh distribution. In a broad non‐Gaussian framework, reverberation is modelled as MEC distribution, which is suitable to characterize non‐Rayleigh reverberation. The received data in trials validate the effectiveness of MEC model. The real data envelops follows K distribution, which is a special case of MEC. So, the MEC can be applied to develop novel signal‐processing algorithms that mitigate or account for the effects of the heavy‐tailed reverberation distributions on the target detection.

The limited sea test data are the main limitation to prove model validation in further.

A very useful model for representing reverberation in shallow‐water.

The MECs in fact represent an attractive set data model for adaptive array, and it provides a theoretic framework to design an optimal or sub‐optimal detector.

The purpose of this paper is to exploit the multiple-Toeplitz matrices reconstruction method combined with quadratic spatial smoothing processing to improve the direction-of-arrival (DOA) estimation performance of coherent signals at low signal-to-noise ratio (SNRs).

An improved multiple-Toeplitz matrices reconstruction method is proposed via quadratic spatial smoothing processing. Our proposed method takes advantage of the available information contained in the auto-covariance matrices of individual Toeplitz matrices and the cross-covariance matrices of different Toeplitz matrices, which results in a higher noise suppression ability.

Theoretical analysis and simulation results show that, compared with the existing Toeplitz matrix processing methods, the proposed method improves the DOA estimation performance in cases with a low SNR. Especially for the cases with a low SNR and small snapshot number as well as with closely spaced sources, the proposed method can achieve much better performance on estimation accuracy and resolution probability.

The study investigates the possibility of reusing pre-existing designs for the DOA estimation of the coherent signals. The proposed technique enables achieve good estimation performance at low SNRs.

The paper includes implications for the DOA problem at low SNRs in communication systems.

The proposed method proved to be useful for the DOA estimation at low SNR.

The continual growth of additive manufacturing has increased tremendously because of its versatility, flexibility and high customization of geometric structures. However, design hurdles are presented in understanding the relationship between the fabrication process and materials microstructure as it relates to the mechanical performance. The purpose of this paper is to investigate the role of build architecture and microstructure and the effects of load direction on the static response and mechanical properties of acrylonitrile butadiene styrene (ABS) specimens obtained via the fused deposition modeling (FDM) processing technique.

Among additive manufacturing processes, FDM is a prolific technology for manufacturing ABS. The blend of ABS combines strength, rigidity and toughness, all of which are desirable for the production of structural materials in rapid manufacturing applications. However, reported literature has varied widely on the mechanical performance due to the proprietary nature of the ABS material ratio, ultimately creating a design hurdle. While prior experimental studies have studied the mechanical response via uniaxial tension testing, this study has aimed to understand the mechanical response of ABS from the materials’ microstructural point of view. First, ABS specimen was fabricated via FDM using a defined build architecture. Next, the specimens were mechanically tested until failure. Then finally, the failure structures were microstructurally investigated. In this paper, the effects of microstructural evolution on the static mechanical response of various build architecture of ABS aimed at FDM manufacturing technique was analyzed.

The results show that the rastering orientation of 0/90 exhibited the highest tensile strength followed by fracture at its maximum load. However, the “45” bead direction of the ABS fibers displayed a cold-drawing behavior before rupture. The morphology analyses before and after tensile failure were characterized by a scanning electron microscopy (SEM) which highlighted the effects of bead geometry (layers) and areas of stress concentration such as interstitial voids in the material during build, ultimately compromising the structural integrity of the specimens.

The ability to control the constituents and microstructure of a material during fabrication is significant to improving and predicting the mechanical performance of structural additive manufacturing components. In this report, the effects of microstructure on the mechanical performance of FDM-fabricated ABS materials was discussed. Further investigations are planned in understanding the effects of ambient environmental conditions (such as moisture) on the ABS material pre- and post-fabrication.

The study provides valuable experimental data for the purpose of understanding the inter-dependency between build parameters and microstructure as it relates to the specimens exemplified strength. The results highlighted in this study are fundamental to the development of optimal design of strength and complex ultra-lightweight structure efficiency.