Robust source localization in shallow water based on vector optimization

Owing to the multipath effect, the source localization in shallow water has been an area of active interest. However, most methods for source localization in shallow water are sensitive to the assumed model of the underwater environment and have poor robustness against the underwater channel uncertainty, which limit their further application in practical engineering. In this paper, a new method of source localization in shallow water, based on vector optimization concept, is described, which is highly robust against environmental factors affecting the localization, such as the channel depth, the bottom reflection coefficients, and so on. Through constructing the uncertainty set of the source vector errors and extracting the multi-path sound rays from the sea surface and bottom, the proposed method can accurately localize one or more sources in shallow water dominated by multipath propagation. It turns out that the natural formulation of our approach involves minimization of two quadratic functions subject to infinitely many nonconvex quadratic constraints. It shows that this problem (originally intractable) can be reformulated in a convex form as the so-called second-order cone program (SOCP) and solved efficiently by using the well-established interior point method, such as the software tool, SeDuMi. Computer simulations show better performance of the proposed method as compared with existing algorithms and establish a theoretical foundation for the practical engineering application.     

聚类分析方法识别水下目标

水下目标的固有振动频率与其本身的材料参数密切相关。如能从测得的目标共振频率反演目标材料参数，结合某些先验知识，便可对目标的某些物理属性作出判断，实现或有助于实现水下目标识别。Ｂａｔａｒｄ等人研究了柱形目标中声速与其固有振动频率间的关系。本文分析并简化了这种关系，然后应用模态识别中的动态聚类方法，由目标共振频率反演目标材料参数，所得结果与实际材料参数符合较好。     

水下目标方位估计的一种新方法

矢量传感器是一种新型水声传感器。介绍了应用矢量传感器确定目标方位的理论和方法，给出了仿真和实验研究结果，讨论了矢量传感器具体体积小、输出信号多、抗各向同性干扰等特性。最后定性地分析了目标方位的方位估值误差。     

智能水下机器人水声精确定位技术的研究

讨论了智能水下机器人水声定位技术,给出系统的组成、定位教学模型。论述了提高定位精度措施,最后给出试验测量结果。