LAUSR

Sparse Bayesian learning (SBL) has been extended to estimate acoustic channel impulse responses (CIRs) at low frequencies, where matched filter (MF)-based CIR estimation suffers from low resolution due to a limited frequency band. In this study, the extended SBL was developed to account for nonuniform noise power in a signal model for the CIR via a formulation that considers inconsistent noise and multiple measurements, which cannot be handled in the conventional SBL. The extended SBL is applied to simulated and measured acoustic data and then compared with the MF and existing SBLs. With the advancement in the schemes, the time resolution and denoising are enhanced; especially, the results of the extended SBL on the simulated data show that it clearly distinguishes the two adjacent arrivals with moderate errors in the estimated time delays. Additionally, as a result of applying it to the measured array data, the extended SBL can achieve a high resolution in the CIR estimation, which retains assured arrivals with a single measurement, while some arrivals are weakened by the insufficient measurement for the extended SBL.