Related Experiment Video
Updated: Sep 9, 2025

Simulating Imaging of Large Scale Radio Arrays on the Lunar Surface
Published on: July 30, 2020
Fast deconvolved beamforming for arbitrary arrays based on off-grid sparse Bayesian learning
Jianli Huang1, Yu Wang1, Zaixiao Gong1
1State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, Chinahuangjianli@mail.ioa.ac.cn, wy@mail.ioa.ac.cn, gzx@mail.ioa.ac.cn, nhq@mail.ioa.ac.cn, wangj@mail.ioa.ac.cn, whb@mail.ioa.ac.cn.
None:
The deconvolved beamforming (dCv) improves spatial resolution without expanding the array aperture but fails for the shift-variant beam pattern and the real targets, which are not located on the sampling grids. To solve them, this Letter extends the off-grid sparse Bayesian learning (OGSBL) to dCv because the generalized convolutional model considers the beam pattern at each angle in beam domain. OGSBL reduces modeling errors by parameterizing sampled locations in coarse grids. Controlling the number of output beams from conventional beamforming to cover the spatial area of interest could accelerate convergence without sacrificing accuracy. The simulation results confirm the good performance.
Related Concept Videos
Deconvolution
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
Beams with Unsymmetric Loadings
The first moment-area theorem determines the slope at any point on the beam. This theorem indicates that the change in slope between two points on a beam...
Beams with Symmetric Loadings
The M/EI...
Fast Fourier Transform
The computational efficiency of the FFT becomes...
Maxwell-Boltzmann Distribution: Problem Solving
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
Linear Approximation in Frequency Domain
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....

