Through BEAMFORMING, the desired signal is estimated by calculating the weighted sum of the input signals of an array of antenna elements. In the classical BEAMFORMING methods, computing the optimal weight vector requires prior knowledge on the direction of arrival (DoA) of the desired signal sources. However, in practice, the DoA of the signal of interest is unknown. In this paper, we introduce two different deep-neural-network-based beamformers which can estimate the signal of interest while suppressing noise and interferences in two/three stages when the DoAs are unknown. Employing deep neural networks (DNNs) such as convolutional neural networks (CNNs) and bidirectional long short-term memory (bi-LSTM) networks enables the proposed method to have better performance than existing methods. In most cases, the output signal to interference and noise ratio (SINR) of the proposed beamformer is more than 10dB higher than the output SINR of the classical beamformers.

BEAMFORMING is one of the most important array signal processing blocks in sonar systems which due to the nature of the environment and conditions of operating, requires using of the robust and adaptive methods to provide the feasible specifications in outputs. In the present paper, the latest methods for the adaptive robust BEAMFORMING such as enhanced and modified covariance matrix methods are investigated and finally, by using of simulation in different scenarios and conditions such as steering vector error, sensors gain and phase perturbation and high power noise and strong interference, their capabilities and abilities are presented and method are evaluated. The results show that the methods of Diagonal Loading, LCMV and LCMV mod in different states are not feasible and CMR and ESB methods in the presence of error of steering vector, strong interference and high power noise and gain and phase distortion are more suitable.

Differential beamformers exhibit effective performance in broadband applications, such as acoustic applications, but they have limited white noise gain. To address this limitation, this paper introduces an adaptive weighting-based algorithm designed to enhance the white noise gain of the differential beamformer by leveraging the minimum variance distortionless response (MVDR) BEAMFORMING technique. For this purpose, differential BEAMFORMING is implemented in two stages: in the first stage, the spatial difference of observations is obtained, and in the second stage, the beamformer is optimized. Subsequently, by calculating the coefficients and combining the differential and MVDR beamformers, the proposed adaptive beamformer is derived. In this beamformer, to construct the output signal, the contribution of the differential and MVDR methods is dynamically adjusted using an adaptive combination coefficient, which is a function of frequency, microphone inter-distance, target angle, and the number of microphones. The proposed beamformer, considering four microphones spaced 2 cm apart reveals a remarkable enhancement in white noise gain by 35 dB and SNR gain by 18 dB at a frequency of 1 kHz. Additionally, the proposed adaptive algorithm demonstrates a 3. 5 dB improvement in directivity factor over its differential counterpart.

The main purpose of this article is to evaluate and to compare the performance metrics, array factor (AF), signal to interference plus noise ratio (SINR), mean square error (MSE), bit error rate (BER), and also computational complexity of different modified blind adaptive BEAMFORMING algorithms based on constrained constant modulus (CCM).Two modified algorithms use adaptive step size mechanisms in the stochastic gradient (SG) algorithm for adjusting the step size. The third one, CCM-RLS, uses recursive least squares (RLS) optimization algorithm which is replaced by the inverse correlation matrix instead of the step size. In the case of a uniform linear array (ULA) and 5 users, one as desired signal and the others as interference signals, simulation results show that the modified algorithms, CCMRLS, CCM-SG-time averaging adaptive step size (TAASS) and CCM-SG-modified adaptive step size (MASS), offer higher performance with respect to conventional CCM-SG, respectively. Comparing the performance of CCM-RLS and adaptive step size versions of CCM-SG show that CCM-RLS converges faster and it can cancel the interferences close to the desired signal, more effectively. Moreover, the resulting SINR level is higher and BER is less than the other methods.However, CCM-SG-MASS and CCM-SG-TAASS have less computational complexity, additions and multiplications.

MIMO systems, and particularly massive MIMO systems, achieve high spectral efficiency by using a large number of antennas. An important issue in these systems is BEAMFORMING. In fully digital baseband BEAMFORMING, an RF chain is required for each antenna, which leads to high cost and power consumption. In analog BEAMFORMING, only one RF chain is used and BEAMFORMING is performed by using phase shifters. This method does not provide optimal spectral efficiency and thus, analog-digital hybrid methods for BEAMFORMING are considered. In this paper, a hybrid BEAMFORMING method is proposed in which the required number of RF chains is much less than fully digital method. The precoder and receiver filter are designed by maximizing the spectral efficiency. To this end, the optimal BEAMFORMING matrix (which contains the right singular vectors of the channel matrix) is approximated by the product of two analog and digital BEAMFORMING matrices. This approximation is improved by an iterative method. The criterion for the proximity of the two matrices is considered to be the Frobenius norm of their differences. In the receiver, the design of the hybrid BEAMFORMING is performed in a similar way, using the mean squares error criterion. Also, to improve the method, a gradient-based algorithm is proposed to further reduce the error. The simulation results show the performance superiority of the proposed method over similar methods as well as its less complexity.

Typical cryptography schemes are not well suited for low complexity types of equipment, e. g. Internet of things (IoT) devices, as they may need high power or impose high computational complexity. Physical (PHY) layer security techniques such as BEAMFORMING (in multiple antennas systems) are possible alternatives to provide security for such applications. In this paper, we consider a network with multiple groups of users as receivers and a transmitter that intends to send different messages to each group. There are also some eavesdroppers (Eavs) at known locations of the environment. The goal of this paper is to find the BEAMFORMING vectors that minimize the total transmitting power while keeping the signal level above a threshold at the exact locations of the legitimate receivers (both angle and range) and keeping it less than another threshold at eavesdropping points. We use frequency diverse arrays (FDA) at the transmitter; thus, the transmitter also needs to determine the frequency that each antenna element must use for data transmission. This condition makes the problem non-convex and so we propose an approximate solution for solving this optimization problem. Simulation results show the performance of the scheme in a particular network setting.

In this paper, an adaptive BEAMFORMING algorithm is introduced. In this algorithm 2M2+7M multiplications are needed in every vector sample which M is the number of the array elements. It is possible to reduce the number of the multiplications to 5M if more simplifications are done which is suitable for practical applications. In this method, adaptive algorithm is applied in pattern space and the ratio of the main beam to the interference beam greater than 50dB is obtained which is acceptable in the considered environmentwith 6dB of SINR.

Microphone array processing techniques have generated a tremendous interest in both theoretical and applied areas, especially, over the past decade. So long as an array performs a type of spatial filtering, the dimension of array will determine the acceptable frequency-range of the incoming waves. Consequently, it IS well known that, miniature microphone arrays are not appropriate for audio applications. In this paper, we introduce a novel technique to utilize miniature microphone arrays for detecting audio signals. Besides the problem of low sensitivity of such arrays in low frequency BEAMFORMING, due to dealing with wideband audio signals, is resolved. We start our discussion with a Delay and-Sum beam former, and extend it to the GSC-based beam former, as an example of popular beamformers. Constant directivity is an important result of the proposed approach