海洋机器人前沿技术中心知识库

海洋声信道参数空间场能够刻画水声信号在海洋中传播的空间分布规律,对水声通信位置选取、水下目标探测及隐身等具有重要指导意义。针对应用压缩感知重构声场时水下机器人测量路径的优化问题,在结合声场特点、压缩感知和水下机器人的运动特点的基础上,提出了一种基于遗传算法的测量优化方法,以提高压缩感知方法的重构精度。首先分析了声场重构中压缩感知测量矩阵的结构,然后结合水下机器人的运动能力限制,定义了遗传算法中适用于水下机器人测量的基因表达、生成方式以及适应度函数。仿真实验中以高斯随机点的旅行商问题和梳状测量路径为对照,结果表明所提方法能够明显提高声场重构的精度,且对不同采样率及不同分布声场的重构都能够保持更高的精度。

The spatial field of ocean acoustic channel parameters can describe the spatial distribution law of underwater acoustic signal propagation in the ocean,which has important guiding significance for underwater acoustic communication location selection, underwater target detection and stealth.For the problem of sampling trajectory optimization in the application of compressive sensing(CS)methods on the acoustic field reconstruction,a sampling optimization method based on a genetic algorithm (GA)is proposed to improve the CS reconstruction accuracy in this paper combining the characteristics of sound field,compressed sensing and the motion characteristics of underwater robot.Firstly,the structure of the CS sampling matrix is analyzed. Then,combining with the kinematic constraint of underwater vehicles,the gene expression and generation method as well as the GA fitness function are defined to support the sampling of underwater vehicles.In simulations,the traveling salesman problem (TSP)- based path from Gaussian random sampling points and the lawnmower sampling path are used for comparison.The results demonstrate that the proposed GA-based sampling method can significantly improve the reconstruction accuracy of acoustic fields.The influences of different sampling rates and different acoustic filed distributions are discussed,which further illustrates the superior performance of the proposed method.