Underwater acoustic channel estimation and impulsive noise mitigation based on sparse Bayesian learning

It is well known that UnderWater Acoustic(UWA) channel is sparse.Sparse Bayesian Learning(SBL) can estimate sparse UWA Channel Impulsive Response(CIR) effectively.Considering the relatively high complexity of SBL,Generalized Approximate Message Passing-Sparse Bayesian Learning(GAMP-SBL) algorithm is incorporated into UWA channel estimation which estimates CIR with message passing in SBL framework and reduce the computational complexity of SBL without losing much performance.Under the environment with impulsive noise,the performance of channel estimation algorithms with the assumption of Gaussian distributed background noise will decrease.By exploiting the sparsity of impulsive noise in the time domain,a GAMP-SBL based channel estimation-impulsive noise mitigation method is proposed to improve the performance of channel estimation under the impulsive noise environment,in which GAMP-SBL is utilized to mitigate the impulsive noise and estimate UWA channel respectively.Simulation results from the 9th Chinese National Arctic Research Expedition verify that the proposed algorithm reduces Normalized Mean Square Error(NMSE) by 18.71%and 6.61%mostly,reduces Bit Error Rate(BER) by 1.66%and 4.05%mostly compared with GAMP-SBL.Besides,the proposed method is more robust than Clipping and BER is less than 10^-2 in 20 dB Signal to Noise Ratio(SNR).