采用时域联合稀疏恢复的多输入多输出水声信道压缩感知估计

Compressed Sensing Estimation of Underwater Acoustic MIMO Channels Based on Temporal Joint Sparse Recovery

多输入多输出(MIMO)水声通信技术可以在极其有限的水声信道频带资源内提高信道容量,但多径和同道干扰的同时存在,使传统信道估计算法如最小二乘算法、压缩感知估计算法的性能急剧下降。考虑到通信数据块间水声信道多径结构存在一定的相关性,该文利用这种数据块间多径结构的时间域相关性建立水声MIMO信道的时域联合稀疏模型,并利用同步正交匹配追踪算法进行多个数据块联合稀疏恢复信道估计,提高MIMO信道多径稀疏位置的检测增益并抑制同道干扰,提高水声MIMO信道的估计性能。仿真和MIMO水声通信海试实验表明了所提方法的有效性。

Multiple-Input-Multiple-Output （MIMO） under water acoustic communication is capable of improving the channel capacity in extremely limited bandwidth. However, the performance of traditional channel estimation algorithms, such as Least Squares （LS） method, Compressed Sensing （CS） method decreases rapidly because of the simultaneous presence of the Co-channel Interference （CoI） and multipath. As the sparse multipath structures between adjacent data blocks exhibit temporal correlation features, in this paper, the temporal correlation of sparse multipath structures is exploited to establish temporal joint sparse MIMO channel estimation model, and the Simultaneous Orthogonal Matching Pursuit （SOMP） algorithm is utilized for compressed sensing estimation of MIMO channels. Simulation and sea trial results validate the effectiveness of the proposed method.