SBL Based on Fixed Noise Power Estimation Applying to Conformal Array Beamforming

Abstract

Underwater motion platforms have limited capacity of carrying and complex movements, and often carry small conformal arrays, so there is a high demand for robust high-resolution beamforming. sparse Bayesian learning (SBL) for beamforming is an algorithm which has robust high-resolution performance. This algorithm needs to estimate the noise power of the received signal, and mainly adopts the asymptotically effective estimation method of maximum likelihood, but this method needs to perform iterative calculation together with the SBL result, and the calculation is complicated. In this paper, SBL based on fixed noise power estimation applying to conformal array beamforming is proposed for the frequency broadband beamforming algorithm of the conformal array mounted on the small motion platform. The simulation and experimental results show that the main lobe and side lobes of this method have high-resolution, and this method does not need to estimate the noise power through iteration, and the calculation amount is also lower than the conventional SBL for beamforming.