An improved temporal multiple sparse Bayesian learning under-ice acoustic channel estimation method

In order to solve the problem of high computational complexity and low estimation accuracy of the Temporal Multiple Sparse Bayesian Learning（TMSBL） channel estimation method at low signal-to-noise ratio,an improved TMSBL under-iceacoustic channel estimation method for Orthogonal Frequency Division Multiplexing（OFDM） is proposed.Firstly,the Singular Value Decomposition（SVD） is used to denoise the receiving pilot matrix.Then,combining with the Least Square（LS） channel estimation method and the denoised receiving pilot matrix,we obtain the prior knowledge of the TMSBL channel estimation method.Finally,by using the sparse characteristic and the stable multipath structure characteristic of under-ice acoustic channel,the TMSBL channel estimation method is used to jointly reconstruct the under-ice acoustic channels with different symbols.The simulation results show that when the energy coefficient is 0.03,compared with TMSBL,the mean squared error of the channel estimation of the improved method is reduced by at least about 2.87 × 10^-5,and the operation time is reduced by at least 20 s.The under-ice communication experimental results of the 11^th Arctic expedition show that the average origin bit error rate of the improved method is slightly lower than that of TMSBL,and the average operation time is reduced about 25 s.The research results demonstrate that the improved method can achieve high precision under-ice acoustic channel estimation and effectively reduce the computational complexity of the system by using the characteristics of under-ice acoustic channel.