Sidelobe Suppression

Handheld ultrasound devices have been widely used for diagnostic applications. The use of the acoustic-field beamforming (AFB) method has been proposed for handheld ultrasound to reduce electricity consumption and avoid battery and unwanted heat issues. However, the image quality, such as the contrast ratio and contrast-to-noise-ratio, are poorer with this technique than with the conventional delay-and-sum method. To address the problems associated with the worse image quality in AFB imaging, in this paper we propose the use of an AFB-based generalized coherence factor (GCF) technique, in which the GCF weighting developed for adaptive beamforming is extended to AFB. Simulation data, experimental results, and in vivo testing verified the efficacy of our proposed AFB-based GCF technique.

In this paper, a compressive sensing approach for wide null steering in partially adaptive arrays is introduced. The proposed method imposes the wide nulls by canceling the whole sidelobes where the interference signals arrive. The problem is first formulated and relaxed to obtain a convex programming problem. The relaxed problem is then solved using iterative re-weighted minimization algorithm to enhance the sparsity in the final solution. Simulations were conducted for one and two wide nulls steering in small and large linear arrays. Results show that the proposed algorithm is capable of steering the required nulls with small number of perturbed array elements.

In a recent paper, the conventional sidelobe canceller radar system was developed by replacing the separate auxiliary antennas by few elements at the center of the main antenna array. The modified system with reused elements was associated with some attenuation in the desired signal due to the emerging correlation between the signals that exists in the main and the reused array elements. This problem was solved by imposing some constraints on the array pattern of the reused elements. In this paper, few of the side elements of the main array are employed as the auxiliary antennas. This new proposed configuration is called sided-elements. Unlike the previous centered-elements configuration, the proposed sided-elements configuration offers more desired features since the pattern of the side elements has sidelobes of similar widths of those of the main array. Moreover, a better diversity is obtained due to the wider separation between the two groups of elements at both sides of the main...

In monopulse radar antennas, the synthesizing process of the sum and difference patterns must be fast enough to achieve good tracking of the targets. At the same time, the feed networks of such antennas must be as simple as possible for efficient implementation. To achieve these two goals, an iterative fast Fourier transform (FFT) algorithm is used to synthesize sum and difference patterns with the main focus on obtaining a maximum allowable sharing percentage in the element excitations. The synthesizing process involves iterative calculations of FFT and its inverse transformations; that is, starting from an initial excitation, the successive improved radiation pattern and its corresponding modified element excitations can be found repeatedly until the required radiation pattern is reached. Here, the constraints are incorporated in both the array factor domain and the element excitation domain. By enforcing some constraints on the element excitations during the synthesizing process,...

Deep learning methods can be found in many medical imaging applications. Recently, those methods were applied directly to the RF ultrasound multi-channel data to enhance the quality of the reconstructed images. In this paper, we apply a deep neural network to medical ultrasound imaging in the beamforming stage. Specifically, we train the network using simulated multi-channel data from two arrays with different sizes, using a variety of direction of arrival (DOA) angles, and test its generalization performance on real cardiac data. We demonstrate that our method can be used to improve image quality over standard methods, both in terms of resolution and contrast. Alternatively, it can be used to reduce the number of elements in the array, while maintaining the image quality. The utility of our method is demonstrated on both simulated and real data.

This paper discusses an ultrasound technique where the echo signals from the array of transducer elements are compressed to as few as two RF channels while still in analog domain, with a much simplified front-end electronics. The method can achieve resolutions well beyond the diffraction limit, which is set by the excitation signal wavelength and numerical aperture of the imaging system. The fundamental principle that underlies this model based imaging technique is the preservation of the spatial frequency information content of the recorded echo signals with the help of pseudo-random apodization function followed by summation. A Verasonics V1 ultrasonic scanner is used to conduct experiments using an anechoic cyst made from gel phantom, immersed in degassed water. The estimated images were compared to those obtained using traditional B-mode delay-and-sum imaging available with the Verasonics V1 ultrasound machine. The estimated images using the proposed imaging technique showed a c...

An optimization method based on compressed sensing is proposed for uniformly excited linear or planar antenna arrays to perturb excitation of the minimum number of array elements in such a way that the required number of nulls is obtained. First, the spares theory is relied upon to formulate the problem and then the convex optimization approach is adopted to find the optimum solution. The optimization process is further developed by using iterative re-weighted l1- norm minimization, helping select the least number of the sparse elements and impose the required constraints on the array radiation pattern. Furthermore, the nulls generated are wide enough to cancel a whole specific sidelobe. Simulation results demonstrate the effectiveness of the proposed method and the required nulls are placed with a minimum number of perturbed elements. Thus, in practical implementations of the proposed method, a highly limited number of attenuators and phase shifters is required compared to other, c...

Deep learning methods can be found in many medical imaging applications. Recently, those methods were applied directly to the RF ultrasound multi-channel data to enhance the quality of the reconstructed images. In this paper, we apply a deep neural network to medical ultrasound imaging in the beamforming stage. Specifically, we train the network using simulated multi-channel data from two arrays with different sizes, using a variety of direction of arrival (DOA) angles, and test its generalization performance on real cardiac data. We demonstrate that our method can be used to improve image quality over standard methods, both in terms of resolution and contrast. Alternatively, it can be used to reduce the number of elements in the array, while maintaining the image quality. The utility of our method is demonstrated on both simulated and real data.

In monopulse radar antennas, the synthesizing process of the sum and difference patterns must be fast enough to achieve good tracking of the targets. At the same time, the feed networks of such antennas must be as simple as possible for efficient implementation. To achieve these two goals, an iterative fast Fourier transform (FFT) algorithm is used to synthesize sum and difference patterns with the main focus on obtaining a maximum allowable sharing percentage in the element excitations. The synthesizing process involves iterative calculations of FFT and its inverse transformations; that is, starting from an initial excitation, the successive improved radiation pattern and its corresponding modified element excitations can be found repeatedly until the required radiation pattern is reached. Here, the constraints are incorporated in both the array factor domain and the element excitation domain. By enforcing some constraints on the element excitations during the synthesizing process,...

This paper introduces an array with a new element structure to achieve asymmetric sidelobe pattern nulling which is a much desired feature in many applications such as communication systems, tracking radars, and imaging. The proposed element structure is built by combining two simple wire dipoles in the horizontal and vertical positions to form a crossed dipole element. The array patterns of the horizontal and vertical dipoles share some common radiation features such as angular null positions which are exploited to provide the required sidelobe nulling. By properly scaling the array pattern of the horizontal dipoles and adding or subtracting its array pattern from those of the vertical dipoles, a new array pattern corresponding to the crossed dipoles elements with controlled sidelobes pattern is obtained. The proposed method is equally applied to the uniformly and nun-uniformly excited arrays. Moreover, the proposed idea is verified by simulating an array with 10 half wavelength crossed dipoles using computer simulation technology microwave studio, and the obtained resultswhich are compared to the theoretical Matlab findings-confidently validate the presented idea.

In this paper, the thinning space is constrained to only outer sub-planar array elements instead of fully filled planar array. Since the amplitude weights of the outer elements have small amplitude excitations, they can be optimized to find the least useful elements and remove them without affecting the desired radiation characteristics. The binary genetic algorithm is used to perform such thinning optimization. Simulation results show that roughly the same performance can be achieved when the number of removed elements in the outer sub-array relative to the total number of the planar array elements does not exceed 19%. In addition, to keep the size of the thinned array equal to that of the original filled array, the perimeter elements were excluded from the thinning process. Also, some constraints on the thinned array pattern are imposed to control the null directions toward interfering signals.

Uniformly Spaced Antenna Array (USAA) with large radiating elements is characterized with complex feed network as well as high sidelobes level (SLL) leading to interference and power wastage. To solve these problems, research works have been carried out using different methodologies, to synthesize sparse Randomly Spaced Antenna Array (RSAA) to reconstruct the desired radiation pattern using fewer radiating elements and suppressed SLL. In this paper, a deterministic Iteratively Reweighted Least Squares (IRLS) algorithm based on the concept of compressed sensing was used to achieve better sparsity through thinning. The SLL was also suppressed using Convex Technique (CT). The performance of the synthesized array was evaluated in terms of sparsity and SLL. Simulation results showed that it has a higher sparsity of 12 elements with SLL of-39.44dB which are 14.29% and 28.72% improvements, respectively compared to previous research work with 14 elements and SLL of-30.64dB.

Aperture domain model image reconstruction (ADMIRE) is a useful tool to mitigate ultrasound imaging artifacts caused by acoustic clutter. However, its lengthy run-time impairs its usefulness. To overcome this drawback, we evaluated the reduced model methods with otherwise similar performance to ADMIRE. We also assessed other approaches to speed up ADMIRE, including the use of different levels of short-time Fourier transform (STFT) window overlap and examining the degrees of freedom of the model fit. In this study, we conducted an analysis of the reduced models, including those using Gram–Schmidt orthonormalization (GSO), singular value decomposition (SVD), and independent component analysis (ICA). We evaluated these reduced models using the data from simulations, experimental phantoms, and in vivo liver scans. We then tested ADMIRE’s performance using full, GSO, SVD, and ICA–fourth-order blind identification (ICA-FOBI) models. The results from simulations, experimental phantoms, and...

In large antenna arrays possibility of getting faults for some of the radiating elements can not be denied at all the times. In such situation the pattern of the array gets distorted mostly with an increasing sidelobe level (SLL) and removal of the nulls, if any, from its desired position. In this paper a healing system using particle swarm optimization (PSO) is developed for these failed antenna arrays. Reconfiguration of the amplitude and phase distribution of the remaining working elements in a failed array can improve the SLL and also maintain the null position. The main purpose of using the PSO technique is its ease of implementation. Compared to its other counterparts, the PSO algorithm is simple, easy to code and a high performance computational technique.

Today, most satellite systems employ reflector antennas to generate narrow beams covering a certain area of Earth’s surface from orbit. However, the performance specification of modern communication systems require an increasing demand on the multi-beam, multi-channel, dual-polarization and beam shaping capabilities, which renders the design of such complex systems to a major challenge. A worldwide view is that Direct Radiating Arrays (DRAs) will be part of the future satellite systems. DRAs can handle these complexities, but the design solutions, based on conventional regular arrays are expensive due to a large number of required array elements (100-1000 in dense array for geosynchronous orbit) and active components, such as low noise and power amplifiers. For this reason, aperiodic or sparse arrays, where the array layout is optimized, constitute a very attractive solution for minimizing the number of elements and thus the cost. To design aperiodic arrays, Array Signal Processing ...

The least square algorithm is often used for the pattern synthesis of uniform phased arrays due to its fast convergence owing to exact matrix-based formulation. This paper presents the pattern synthesis of linear array with nonuniform inter-element spacing using the weighted least square algorithm. The computed results demonstrate that the algorithm has direct control over the desired mainlobe width, the sidelobe level, the position and width of the notch even when the spacing between the antenna elements is non-uniform or random.