Iterative reweighted atomic norm minimization based two-dimensional multiple-snapshot grid-free compressive beamforming with planar microphone array

Abstract

Compressive beamforming with a planar microphone array can effectively estimate the two-dimensional directions-of-arrival and quantify the strengths of acoustic sources. Due to the superiorities of overcoming the basis mismatch issue of the conventional grid-based method and improving the performance of the single-snapshot grid-free method, the multiple-snapshot grid-free method has become the current research focus. Its existing atomic norm minimization (ANM) based strategy blocks high resolution because it cannot work well for sources with a small separation. This paper commits itself to remedying this drawback. After revealing the cause for this drawback, we present an iterative reweighted ANM (IRANM) approach. Both simulations and experiments demonstrate that compared with the ANM-based two-dimensional multiple-snapshot grid-free compressive beamforming, the IRANM-based one enjoys not only the enhanced resolution but also the stronger denoising ability and the higher identification accuracy.

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge

“…These shortcomings obstruct its application in PD localization. Several improved algorithms are developed to overcome the limitations of CBF [ 28 , 29 , 30 , 31 ], and are expected to be employed for localizing the PD.…”