scholarly journals Two‐dimensional adaptive beamforming for large planar array antennas based on weight matrix reconstruction

2021 ◽  
Author(s):  
Wei Gao ◽  
Xiaoming Li ◽  
Zheng Liu ◽  
Ziqiang Meng ◽  
Lei Ran
Keyword(s):  
Adaptive Beamforming ◽  
Weight Matrix ◽  
Two Dimensional ◽  
Array Antennas ◽  
Matrix Reconstruction ◽  
Planar Array

Convolutional neural network for 2D adaptive beamforming of phased array antennas with robustness to array imperfections

2021 ◽  
pp. 1-7
Author(s):  
Tarek Sallam ◽  
Ahmed M. Attiya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Phased Array ◽  
Weight Vector ◽  
Adaptive Beamforming ◽  
Phased Array Antenna ◽  
Phased Array Antennas ◽  
Array Antennas ◽  
Optimum Weight ◽  
High Computational Complexity

Abstract Achieving robust and fast two-dimensional adaptive beamforming of phased array antennas is a challenging problem due to its high-computational complexity. To address this problem, a deep-learning-based beamforming method is presented in this paper. In particular, the optimum weight vector is computed by modeling the problem as a convolutional neural network (CNN), which is trained with I/O pairs obtained from the optimum Wiener solution. In order to exhibit the robustness of the new technique, it is applied on an 8 × 8 phased array antenna and compared with a shallow (non-deep) neural network namely, radial basis function neural network. The results reveal that the CNN leads to nearly optimal Wiener weights even in the presence of array imperfections.

scholarly journals Thermos Array: Two-Dimensional Sparse Array with Reduced Mutual Coupling

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Lei Sun ◽  
Minglei Yang ◽  
Baixiao Chen
Keyword(s):  
Closed Form ◽  
Degrees Of Freedom ◽  
Mutual Coupling ◽  
Doa Estimation ◽  
Two Dimensional ◽  
Spatial Smoothing ◽  
Planar Arrays ◽  
Half Wavelength ◽  
Planar Array ◽  
Small Spacing

Sparse planar arrays, such as the billboard array, the open box array, and the two-dimensional nested array, have drawn lots of interest owing to their ability of two-dimensional angle estimation. Unfortunately, these arrays often suffer from mutual-coupling problems due to the large number of sensor pairs with small spacing d (usually equal to a half wavelength), which will degrade the performance of direction of arrival (DOA) estimation. Recently, the two-dimensional half-open box array and the hourglass array are proposed to reduce the mutual coupling. But both of them still have many sensor pairs with small spacing d, which implies that the reduction of mutual coupling is still limited. In this paper, we propose a new sparse planar array which has fewer number of sensor pairs with small spacing d. It is named as the thermos array because its shape seems like a thermos. Although the resulting difference coarray (DCA) of the thermos array is not hole-free, a large filled rectangular part in the DCA can be facilitated to perform spatial-smoothing-based DOA estimation. Moreover, it enjoys closed-form expressions for the sensor locations and the number of available degrees of freedom. Simulations show that the thermos array can achieve better DOA estimation performance than the hourglass array in the presence of mutual coupling, which indicates that our thermos array is more robust to the mutual-coupling array.

Analysis and design methods of planar array antennas with slotted waveguides taking into account mutual coupling

1989 ◽  
Vol 72 (10) ◽  
pp. 103-110 ◽  
Author(s):  
Yoshihiko Konishi ◽  
Hitoshi Mizutamari ◽  
Shin-Ichi Sato ◽  
Seiji Mano ◽  
Takashi Katagi
Keyword(s):  
Mutual Coupling ◽  
Design Methods ◽  
Analysis And Design ◽  
Array Antennas ◽  
Planar Array

scholarly journals Bayesian Spatial Autoregressive for Reducing Blurring Effect in Image

2007 ◽  
Vol 11 (3) ◽  
pp. 308-311
Author(s):  
Siana Halim ◽  
Keyword(s):  
Error Estimate ◽  
Weight Matrix ◽  
Block Bootstrap ◽  
Two Dimensional ◽  
Large Matrix ◽  
Specific Manner ◽  
Spatial Autoregressive ◽  
The Difference ◽  
Blurring Effect ◽  
Regular Texture

We apply the Bayesian Spatial Autoregressive, which is developed by Geweke and LeSage, for reducing the blurring effect in the image. This blurring effect, particularly comes from the synthesizing semi regular texture via, e.g., two dimensional block bootstrap. We model the error, i.e., the difference between the true image and the synthesis one, as the Bayesian Spatial Autoregressive (SAR). Moreover, the weight matrix is defined in a specific manner, such that the problem in the computational for a very large matrix can be avoided. Finally, we use the error estimate, as the result of Bayesian SAR modelling, for reducing the blurring effect in the synthesis image.

scholarly journals Design and Analysis of Novel Fractal Linear and Planar Array Antennas for Satellite Applications

2016 ◽  
Vol 5 (3) ◽  
pp. 56 ◽  
Author(s):  
V. A. Sankar ◽  
P. V. Y. Jayasree
Keyword(s):  
Design Methodology ◽  
Beam Width ◽  
Side Lobe ◽  
Circular Ring ◽  
Geometric Design ◽  
Side Lobe Level ◽  
Array Antennas ◽  
Repetitive Nature ◽  
Satellite Applications ◽  
Planar Array

This article proposed a new geometric design methodology for the systematic expansion of fractal linear and planar array antennas. Using this proposed geometric design methodology any deterministic polygon shape can be constructed. In this article, two element fractal linear and triangular array antennas are examined using proposed methodology up to four iterations of two expansion factors. Due to the repetitive nature of the proposed geometric design methodology, both linear and planar fractal arrays shows multi-beam behavior with excellent array factor properties. The behavior of the proposed arrays shows better performance than linear and planar fractal array antennas generated by concentric circular ring sub array geometric generator. Triangular planar fractal array of expansion factor two at fourth iteration achieved a single valued beam width of 3.80 with -31.6 side lobe level. The suggested fractal arrays are analyzed and simulated by MATLAB-13 programming.

Sign in / Sign up

Export Citation Format

Share Document