复杂水下环境中自适应粒子群优化的SINS/DVL组合导航方法  

SINS/DVL integrated navigation method based on adaptive particle swarm optimization in complex underwater environment

作　　者：王迪 王冰[1] 黄浩乾 王广才 刘锡祥[3] 姚逸卿 WANG Di;WANG Bing;HUANG Haoqian;WANG Guangcai;LIU Xixiang;YAO Yiqing(College of Energy and Electrical Engineering,Hohai University,Nanjing 211100,China;College of Mechanical and Vehicle Engineering,Hunan University,Changsha 410006,China;School of Instrument Science and Engineering,Southeast University,Nanjing 210096,China)

机构地区：[1]河海大学能源与电气学院,南京211100 [2]湖南大学机械与运载工程学院,长沙410006 [3]东南大学仪器科学与工程学院,南京210096

出　　处：《中国惯性技术学报》2023年第10期1023-1029,1036,共8页Journal of Chinese Inertial Technology

基　　金：国家自然科学基金(62303157);中国博士后科学基金面上项目(2022M720993);江苏省卓越博士后计划资助项目(2023ZB042)。

摘　　要：针对复杂水下环境中基于捷联惯性导航系统/多普勒计程仪(SINS/DVL)组合导航系统受干扰导致定位精度下降的问题,提出一种自适应粒子群优化的SINS/DVL组合导航方法。首先,针对模型中方差估计不一致问题,构建了基于状态变换原理的SINS/DVL导航模型;其次,考虑到复杂水下环境中基于卡尔曼滤波的噪声难以估计问题,构造粒子滤波适应度函数,并设计了一种自适应粒子群优化算法;最后,对所提方法进行了仿真实验和湖试实验。实验结果表明:所提方法可以解决复杂水下环境中SINS/DVL组合导航系统受干扰导致定位精度不高的问题。相比于卡尔曼滤波方法、Huber鲁棒滤波方法,所提方法的水平位置误差最大值分别减少了49.56%和41.20%。To solve the problems that the position accuracy of strapdown inertial navigation system/Doppler velocity log(SINS/DVL)integrated navigation system is degraded by interference in complex underwater environment,a SINS/DVL integrated navigation method based on adaptive particle swarm optimization is proposed.Firstly,SINS/DVL navigation model based on the principle of state transformation is constructed to solve the problem of inconsistent variance estimation in the model.Secondly,considering the difficulty of noise estimation based on Kalman filter in complex underwater environment,the fitness function of particle filter is constructed,and an adaptive particle swarm optimization algorithm is designed.Finally,the proposed method is tested by simulation and lake test.The experimental results show that the proposed method can solve the problem of poor positioning accuracy caused by interference of SINS/DVL integrated navigation system in complex underwater environment.Compared with Kalman filtering method and Huber robust filtering method,the maximum horizontal position error of the proposed method is reduced by 49.56%and 41.20%,respectively.

关 键 词：捷联惯性导航系统/多普勒计程仪 组合导航 粒子群优化 鲁棒滤波 

分 类 号：U666.1[交通运输工程—船舶及航道工程]