流形学习在浅海水声通信中的应用

在复杂的浅海环境中，水声信道具有强烈的空变和时变特性，致使水声通信系统的鲁棒性很难得到保证。该文不同于依赖复杂信道编码和信道均衡手段的传统水声通信算法，将流形学习思想应用于高维海洋环境参数空间刻画及信号空间映射中，为水下数据传输提出创新方案。从声场角度出发，结合浅海实验数据，分析通信信号时空起伏特性，研究环境参数空间和声场信号空间的内在关系，提出了基于非线性流形学习算法增加合理的物理约束，结合信道稀疏特性，对于高维非线性水声信号系统的冗余维度信息进行维数约简，映射到稳定的低维目标空间，降低信道时空起伏对通信系统的影响。仿真和实验结果验证了算法的可靠性和有效性。

Application of Manifold Learning to Shallow Water Acoustic Communication

Sallow water acoustic channel is severely affected by time-space variation, which destroys the robustness of underwater acoustic communication system. By introducing manifold learning in the analysis of high dimensional underwater environment and channel equalization processing, a novel underwater acoustic communication algorithm is presented. By establishing the mapping between environment parameter space and signal space, several physical restrictions can be posed on non-linear manifold learning algorithm. Moreover, the sparse property can reduce the dimension of underwater acoustic channel in order to exclude high dimensional non-linear noise from channel time-space variation. Both sound field analysis and shallow water experimental data verify the validity and the robustness of the proposed algorithm.