Deconvolved frequency-difference beamforming for a linear array

Abstract

Frequency-difference beamforming (FDB) provides a robust estimation of wave propagation direction by shifting signal processing to a lower frequency which, however, produces a decline in the spatial resolution. In this letter, the beam pattern of FDB for a distant point source is proved to be shift invariant and therefore can be regarded as the point spread function corresponding to FDB's beam output. Then, deconvolved frequency-difference beamforming (Dv-FDB) is proposed to improve array performance. Dv-FDB yields a narrower beam and lower sidelobe levels while maintaining robustness. The superior performance of Dv-FDB is verified by simulations and experimental data.

Deconvolved Fractional Fourier Domain Beamforming for Linear Frequency Modulation Signals

“…In recent years, DCBF has been widely used for various arrays [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Deconvolution is also used as postprocessing for other beamforming methods, such as Chebyshev weighting beamforming [ 39 ] and frequency-difference beamforming [ 40 ], to optimize their spatial spectrum and to improve resolution.…”

Research on High Robustness Underwater Target Estimation Method Based on Variational Sparse Bayesian Inference

“…Simulation and experimental results showed that Dv-FDB was superior to FDB in higher resolution and lower sidelobes while maintaining robustness. Existing R-L methods are limited to arrays with offset-invariant beam patterns [13]. Byun et al (2020) proposed a multi-constraint method for matching field processing (MFP) to address the uncertainty of the array tilt, and the experimental results verified the robustness of MFP.…”