水声传感器网络节点自定位的遗传算法优化研究  

Node Self-Localization of Underwater Acoustic Sensor Network by Genetic Algorithm Optimization

作　　者：谢远党[1] 徐荣伟 张华[1] 

机构地区：[1]浙江海洋学院机电工程学院,浙江舟山316004 [2]舟山万达船舶设计有限公司,浙江舟山316101

出　　处：《浙江海洋学院学报（自然科学版）》2011年第4期349-353,共5页Journal of Zhejiang Ocean University(Natural Science Edition)

摘　　要：水声传感器网络节点自定位技术是传感器网络在海洋环境监测应用的基础。针对质心算法在随机分布中定位精度较低的缺点,采用信标节点与未知节点之间的距离作为约束,并对该约束采用泰勒级数方式展开;由此建立相应的数学模型,通过遗传算法对该模型进行优化;此外,根据信号传播特点,采用的是等高线传输模型。仿真结果表明,该方法能够实现水声传感器网络未知节点的有效定位。The applications in the marine environmental monitoring is based on the node self-localization of underwater acoustic sensor network. The constraint of distance between beacon nodes and unknown nodes is used Taylor series expansion, because of a lower positioning accuracy in random distribution of centroid algorithm. Genetic algorithm is optimized the corresponding mathematical model; in addition, the transmission model contour is used according to the signal transmission characteristics. Simulation results show that the method can realize the unknown nodes of acoustic sensor network effectively positioning.

关 键 词：水声传感器网络 遗传算法 节点自定位 泰勒级数 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]