﻿ 一种大规模水下声传感器网络定位技术
 舰船科学技术  2018, Vol. 40 Issue (7): 106-110 PDF

1. 中国人民解放军92942部队，北京 100161;
2. 中国人民解放军91977部队，北京 100036

The network location technology of large scale underwater acoustic sensor
WANG Lian-wen1, YAO Yao2, DU Hong-song2, WANG Han2
1.  No.92942 Unit of PLA, Beijing 100161, China;
2. No.91977 Unit of PLA, Beijing 100036, China
Abstract: Aiming at the problem of high cost and difficulty in the placement of the underwater acoustic sensor network (UWSN) reference nodes, this paperstudieda localization method based on recursive scheme for large scale underwater sensor network.Recursive scheme isa method that some common nodes are located by a small amount of initial reference node, then nodes that were successfully positioned will evaluatethe accuracy of the location results, the nodes with high positioning accuracy will be upgraded to reference nodes to participate in the positioning of the remaining common nodes.Results show thatwith a certain precision loss as the cost, the recursive positioning scheme significantly improves the positioning coverage, and combined with three dimensional Euclidean (3D Euclidean) distance estimation the recursive positioning scheme can further enhance the localization coverage.
Key words: underwater acoustic sensor networklocation     recursive     three dimensional Euclidean
0 引　言

1 迭代定位机制 1.1 网络结构

 图 1 迭代定位网络结构图 Fig. 1 The structure diagram of iterative location network

1.2 定位步骤

1）锚节点获得自身位置并泛洪广播定位信息的阶段

2）普通节点定位解算的阶段

 $\left\{ {\begin{array}{*{20}{c}} {{{(x - {x_1})}^2} + {{(y - {y_1})}^2} + {{(z - {z_1})}^2} = {r_1}^2} {\text{，}}\\ {{{(x - {x_2})}^2} + {{(y - {y_2})}^2} + {{(z - {z_2})}^2} = {r_2}^2} {\text{，}}\\ {{{(x - {x_3})}^2} + {{(y - {y_3})}^2} + {{(z - {z_3})}^2} = {r_3}^2} {\text{，}}\\ {{{(x - {x_4})}^2} + {{(y - {y_4})}^2} + {{(z - {z_4})}^2} = {r_4}^2}{\text{。}} \end{array}} \right.$ (1)

 $\begin{split}&\left[ {\begin{array}{*{20}{c}} {\left( {{x_2} - {x_1}} \right)}&{\left( {{y_2} - {y_1}} \right)}&{\left( {{z_2} - {z_1}} \right)} \\ {\left( {{x_3} - {x_2}} \right)}&{\left( {{y_3} - {y_2}} \right)}&{\left( {{z_3} - {z_2}} \right)} \\ {\left( {{x_4} - {x_3}} \right)}&{\left( {{y_4} - {y_3}} \right)}&{\left( {{z_4} - {z_3}} \right)} \\ {\left( {{x_1} - {x_4}} \right)}&{\left( {{y_1} - {y_4}} \right)}&{\left( {{z_1} - {z_4}} \right)} \end{array}} \right]*\left[ \begin{gathered} x \hfill \\ y \hfill \\ z \hfill \\ \end{gathered} \right] = \\&\left[ {\begin{array}{*{20}{c}} {\left( {r_1^2 - r_2^2 + d_2^2 - d_1^2} \right)/2} \\ {\left( {r_2^2 - r_3^2 + d_3^2 - d_2^2} \right)/2} \\ {\left( {r_3^2 - r_4^2 + d_4^2 - d_3^2} \right)/2} \\ {\left( {r_4^2 - r_1^2 + d_1^2 - d_4^2} \right)/2} \end{array}} \right]{\text{。}}\end{split}$ (2)

3）普通节点定位结果自评价阶段

 $\eta = \left\{ {\begin{array}{*{20}{c}}1{\text{，}}\\{1 - \frac{{\sum\limits_i {\left| {{{(u - {x_i})}^2} + {{(v - y{}_i)}^2} + {{(w - {z_i})}^2} - {l^2}_i} \right|} }}{{\sum\limits_i {{{(u - {x_i})}^2} + {{(v - y{}_i)}^2} + {{(w - {z_i})}^2}} }}}{\text{。}}\end{array}} \right.\begin{array}{*{20}{c}}{\begin{array}{*{20}{c}}{}{\{ {\text{初始参考节点}}\} }\\{\{ {\text{普通节点}}\} }\end{array}}\\\end{array}$ (3)

4）迭代定位阶段

1.3 迭代算法流程
 图 2 迭代算法流程图 Fig. 2 The flow chart of iterative algorithm
2 三维欧几里得算法 2.1 算法描述

 图 3 三维欧几里得距离估计示意图 Fig. 3 The schematic figure of 3D Euclidean distance estimation

 图 4 相对坐标法三维欧几里得距离估计原理图 Fig. 4 The schematic diagram of 3D Euclidean distance estimation in

 $\left\{ \begin{gathered} {({x_d} - {\rm{ }}{d_{BC}})^2} + {\rm{ }}y_d^2 = {\rm{ }}d_{CD}^2 \hfill {\text{，}}\\ x_d^2 + {\rm{ }}y_d^2 = {\rm{ }}d_{BD}^2 {\text{，}}\hfill \\ \end{gathered} \right.$ (4)

 $\left\{ \begin{gathered} x_a^2 + {\rm{ }}y_a^2 + {\rm{ }}z_a^2 = {\rm{ }}d_{AB}^2{\text{，}} \hfill \\ ({x_a} - {\rm{ }}{d_{BC}})2 + {\rm{ }}y_a^2 + {\rm{ }}z_a^2 = {\rm{ }}d_{AC}^2 {\text{，}}\hfill \\ {({x_a} - {\rm{ }}{x_d})^2} + {({y_a} - {\rm{ }}{y_d})^2} + {\rm{ }}z_a^2 = {\rm{ }}d_{AD}^2 {\text{。}}\hfill \\ \end{gathered} \right.$ (5)

2.2 算法流程

 图 5 3DEculidean算法流程 Fig. 5 The flow of 3D Euclidean algorithm
3 仿真结果

3.1 节点部署情况

 图 6 传感器节点分布图示例 Fig. 6 Example of sensor node distribution diagram
3.2 节点密度与节点数量关系

 图 7 节点密度与节点数量关系 Fig. 7 The relationship between node density and number of node
3.3 不共线方式的选取

 图 8 最大余弦值与定位误差关系图 Fig. 8 Relation diagram of maximum cosine value and positioning error

3.4 定位覆盖率
 图 9 节点数量与定位覆盖率关系图 Fig. 9 Relation diagram of node number and location coverage

3.5 定位误差

 图 10 节点数量与定位误差关系图 Fig. 10 Relation diagram of node number and positioning error

4 结　语

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