基于IMM算法的多AUV协同定位水声传播延迟处理方法  

Treatment Method for Multi-AUV Cooperative Positioning Underwater Acoustic Propagation Delay Based on IMM Algorithm

作　　者：陈世杰 刘锡祥[1,2] 黄永江[1,2] 章彩霞 陶育杰 童金武 CHEN Shijie;LIU Xixiang;HUANG Yongjiang;ZHANG Caixia;TAO Yujie;TONG Jinwu(School of Instrument Science&Engineering,Southeast University,Wuxi 214026,China;Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology,Ministry of Education,Nanjing 210096,China;School of Innovation and Entrepreneurship,Industrial Center,Nanjing Institute of Engineering,Nanjing 211167,China)

机构地区：[1]东南大学仪器科学与工程学院,江苏无锡214026 [2]微惯性仪表与先进导航技术教育部重点实验室,江苏南京210096 [3]南京工程学院工业中心创新创业学院,江苏南京211167

出　　处：《水下无人系统学报》2023年第2期259-268,277,共11页Journal of Unmanned Undersea Systems

基　　金：国家自然科学基金(51979041,61973079);南京工程学院人才引进基金(YKJ202043);东南大学微惯性仪表与先进导航技术教育部重点实验室(B类)开放基金资助项目(SEU-MIAN-202102)。

摘　　要：针对主从式自主水下航行器(AUV)协同定位系统存在的水声探测与通信的延迟问题,提出一种基于交互式多模型(IMM)算法的时间延迟处理方法。首先建立多AUV系统的协同定位计算模型,针对系统非线性运动方程与非线性量测,分析扩展卡尔曼滤波(EKF)协同定位结果因水声信号传播延迟产生的定位误差;其次阐述常规延迟扩展卡尔曼滤波(DEKF)在处理时间延迟环节中无法实现对机动性目标AUV运动状态的精准跟踪问题;最终设计IMM-DEKF算法,选择适当的运动模型作为子滤波器,利用新息更新模型概率,精确跟踪主AUV运动状态,降低从AUV滤波器中对主AUV状态值的估计误差,实现整体协同系统定位精度的提高。仿真结果验证了所提算法在常规EKF的基础上有效提高了从AUV滤波器对主AUV航迹预测精度,使得协同定位系统的整体定位精度得到提升。To address the delay problem of underwater acoustic detection and communication in a master-slave autonomousundersea vehicle(AUV)cooperative positioning system,a time-delay processing method based on an interacting multiplemodel(IMM)algorithm is proposed.First,a cooperative positioning calculation model for a multi-AUV system is established.Aiming at the nonlinear motion equation and nonlinear measurement of the system,the positioning error caused by thepropagation delay of underwater acoustic signal in the cooperative positioning result of extended Kalman filter(EKF)isanalyzed.Second,the problem that delay EKF(DEKF)cannot accurately track the motion state of the maneuverable targetAUV in handling time delay is described.Finally,the IMM-DEKF algorithm is designed,the appropriate motion models are selected as the sub filters,the model probability is updated via innovation,the motion states of the main AUVs are accuratelytracked,the estimation errors of the state value of the main AUVs from the slaver AUV’s filter are reduced,and the positioningaccuracy of the overall cooperative system is improved.The simulation results verify that the proposed algorithm effectivelyimproves the prediction accuracy of the main track of the AUVs from the slave AUV filter based on the conventional EKF andimproves the overall positioning accuracy of the cooperative positioning system.

关 键 词：自主水下航行器 传播延迟 协同定位 交互多模型 扩展卡尔曼滤波 

分 类 号：TJ630.1[兵器科学与技术—武器系统与运用工程] TB71.2[一般工业技术—真空技术]