异时量测序贯处理的多AUV协同导航

计算机工程与应用 ›› 2011, Vol. 47 ›› Issue (31): 12-16.

异时量测序贯处理的多AUV协同导航

卢健1，2，徐德民1，张福斌1，张立川1

1.西北工业大学航海学院，西安 710072
2.西安工程大学电信学院，西安 710048

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2011-11-01 发布日期:2011-11-01

Cooperative navigation of asynchronous measurements sequential processing for multiple AUVs

LU Jian1，2，XU Demin1，ZHANG Fubin1，ZHANG Lichuan1

1.College of Marine Engineering，Northwestern Polytechnical University，Xi’an 710072，China
2.School of Electronics and Information，Xi’an Polytechnic University，Xi’an 710048，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-11-01 Published:2011-11-01

摘要/Abstract

摘要： 利用多个体之间的相对信息提高个体的定位精度的协同导航是一个很值得探讨的问题。自治水下航行器（AUV）在移动长基线（MLBL）网络中获取各应答器的位置信息并解算出相对各应答器的距离量测后，再通过贝叶斯滤波算法集中式提高其定位精度。但是，集中式利用量测的方法没有考虑由于水下环境的宽广性和水下传感器的限制导致的声信号在AUV和应答器间的往返时间内AUV的移动和应答器间位置的差异所带来的影响。首先以并行滤波的形式总结了集中式扩展卡尔曼滤波（EKF）协同导航方法，并针对集中式利用量测的不利因素，提出了按照异时量测的产生顺序即时更新AUV状态的更有效的序贯EKF协同导航算法，最后在仿真中将两种处理方法进行了比较。

关键词: 定位, 协同导航, 自治水下航行器, 移动长基线, 扩展卡尔曼滤波, 仿真

Abstract: Cooperative navigation which can improve the individual localization accuracy through utilizing the relative information among multiple individuals is a problem worthy of discussing.An Autonomous Underwater Vehicle（AUV） can acquire the location information of the responders through depending on the Moving Long BaseLine（MLBL） network and calculate the relative distances between the responders and the AUV，and then improve its localization accuracy synchronously through a Bayesian filter.However，the method of the centralized use of measurements does not consider the influences brought by the movement of the AUV within the round-trip time of the acoustic signals spreading between the AUV and the responders and the location differences of the responders caused by the breadth of the underwater environment and the limitation of underwater sensor.In this paper，a cooperative navigation algorithm based on the centralized Extended Kalman Filter（EKF） is summarized in the form of a parallel filter firstly，and then a more efficient cooperative navigation algorithm that updates the AUV state immediately according to the producing order of asynchronous measurements based on the sequential EKF is proposed considering the adverse factors of the centralized use of measurements，and two processing algorithms are compared in the simulation lastly.

Key words: localization, cooperative navigation, autonomous underwater vehicle, moving long baseline, Extended Kalman Filter（EKF）, simulation

卢健1，2，徐德民1，张福斌1，张立川1. 异时量测序贯处理的多AUV协同导航[J]. 计算机工程与应用, 2011, 47(31): 12-16.

LU Jian1，2，XU Demin1，ZHANG Fubin1，ZHANG Lichuan1. Cooperative navigation of asynchronous measurements sequential processing for multiple AUVs[J]. Computer Engineering and Applications, 2011, 47(31): 12-16.

[1]	黄逸磊，夏志杰，王诣铭. 强关系型社交媒体平台不实信息传播模型研究[J]. 计算机工程与应用, 2021, 57(9): 126-133.
[2]	王博，宋丹，王洪玉. 无人机自主巡检系统的关键技术研究[J]. 计算机工程与应用, 2021, 57(9): 255-263.
[3]	徐少杰，曹雏清，王永娟. 视觉SLAM在室内动态场景中的应用研究[J]. 计算机工程与应用, 2021, 57(8): 175-179.
[4]	周彦，旷鸿章，牟金震，王冬丽，刘宗明. 面向半稠密三维重建的改进单目ORB-SLAM[J]. 计算机工程与应用, 2021, 57(8): 180-184.
[5]	马巧梅，王明俊，梁昊然. 复杂场景下基于改进YOLOv3的车牌定位检测算法[J]. 计算机工程与应用, 2021, 57(7): 198-208.
[6]	赵凡，张琳，闻治泉，杨林林，蔺广逢. 一种直接高效的自然场景汉字逼近定位方法[J]. 计算机工程与应用, 2021, 57(6): 159-167.
[7]	李中道，刘元盛，常飞翔，张军，路铭. 室内环境下UWB与LiDAR融合定位算法研究[J]. 计算机工程与应用, 2021, 57(6): 260-266.
[8]	於小杰，贺勇，刘盛华. 一种用于无人机室内定位的改进ORB光流算法[J]. 计算机工程与应用, 2021, 57(4): 266-271.
[9]	程帅，吴华锋，梅骁峻. 交替非负约束框架的海洋传感网协同定位[J]. 计算机工程与应用, 2021, 57(23): 129-136.
[10]	谢兄，杨金鹏. YOLO-wLU：考虑定位不确定性的目标检测算法[J]. 计算机工程与应用, 2021, 57(22): 223-231.
[11]	杨世强，白乐乐，赵成，王国栋，李德信. 一种改进的Qtree_ORB算法[J]. 计算机工程与应用, 2021, 57(20): 82-89.
[12]	陈金鑫，沈文忠. 基于EL-YOLO的虹膜图像人眼定位及分类算法[J]. 计算机工程与应用, 2021, 57(17): 217-223.
[13]	杨世强，范国豪，白乐乐，赵成，李德信. 基于几何约束的室内动态环境视觉SLAM[J]. 计算机工程与应用, 2021, 57(16): 203-212.
[14]	李高源，王晋宇，张长弓，冯博迪，高宇歌，杨海涛. SAR图像仿真方法研究综述[J]. 计算机工程与应用, 2021, 57(15): 62-72.
[15]	成立明，陈建新，陈瑞，陈志敏. 融合自监督单目图像深度估计的视觉里程计[J]. 计算机工程与应用, 2021, 57(15): 230-236.