使用稀疏贝叶斯学习的水声多途信道盲估计

提出了一种使用稀疏贝叶斯学习(SBL)的多途信号盲解卷积方法对水声多途信道的信道脉冲响应(CIR)进行盲估计。该方法利用垂直阵和多频SBL获得宽带舰船声源在不同垂直到达角上的复数域多频点信号,取其相位对垂直阵接收信号匹配滤波,得到每条路径上的CIR,将多路径CIR相干叠加得到最终的多途CIR结果。仿真与海试数据处理结果表明,相比于原有的基于交替投影的多途信号盲解卷积方法,所提出方法有以下几个好处:(1)无需准确预估多途信号数目;(2)分离的多途信号的方位更准确且信号相位更可靠;(3)有效获取了舰船与阵列之间的CIR.并且将弱路径CIR的平均时间估计误差从4.7 ms缩小到1.0 ms.显著提高了弱路径CIR的时间估计精度。使用稀疏贝叶斯学习的多途信号盲解卷积方法能够有效提高多途环境下水声信道盲估计的性能。 

Blind estimation of underwater acoustic multipath channel using sparse Bayesian learning

A blind deconvolution method using multiple beam outputs produced by Sparse Bayesian Learning(SBL) is proposed to estimate the Channel Impulse Response(CIR) of underwater acoustic multipath channel.The method firstly uses a Vertical Linear Array(VLA) and the multi-frequency SBL algorithm to obtain the multi-frequency signal in the complex domain at each vertical arriving angle;and then,it uses the phases picked out from the signal to matched filter the received signals at each hydrophone in the VLA,so as to obtain the CIR corresponding to a vertical arriving angle,which in fact corresponds to a propagation path in the waveguide;finally,it coherently adds the CIRs at all paths to get the final CIR result.Numerical simulations and the sea trial data show that the proposed method has several advantages when compared with the existing multi-path blind deconvolution algorithm based on alternate projection:(1) it does not need to accurately estimate the number of multiple paths in the waveguide;(2) the estimated angle of the multi-path signals is more accurate and the estimated signal phase is more reliable;(3) the CIR between the vessel and the VLA is effectively obtained,and the average time error of the CIR of the weak paths is reduced from 4.7 ms to 1.0 ms,so the time accuracy of the CIR of the weak path is significantly improved.As a result,it can be concluded that the proposed blind deconvolution method using SBL can effectively improve the performance of the blind estimation of CIR in underwater acoustic multipath channel.