基于水声通信延迟的多UUV协同定位算法

发布时间：2018-07-31 16:15

【摘要】：针对多水下自治机器人(unmanned underwater vehicle,UUV)协同定位过程中水声通信延迟造成的定位失效问题,提出了一种基于状态估计均方误差最小的延时扩展卡尔曼滤波(delayed extended Kalman filter,DEKF)定位误差修正方法。首先建立考虑水声通信延迟的系统状态方程,利用状态转移矩阵推导系统等效量测方程,然后给出多UUV考虑水声通信延迟的扩展卡尔曼滤波(extended Kalman filter,EKF)定位方法并分析其不足;最后在EKF方法的基础上,分析量测信息延迟对状态估计的影响,建立系统真实量测模型,设计基于状态估计均方误差最小的DEKF算法。仿真结果表明,该方法能够有效地修正多UUV协同定位中由于水声通信延迟造成的定位误差,在工程实践中具有一定参考意义。
[Abstract]:Aiming at the problem of localization failure caused by underwater acoustic communication delay in multi-underwater autonomous vehicle (unmanned underwater) cooperative positioning process, a delayed extended Kalman filter (delayed extended Kalman filter) positioning error correction method based on minimum mean square error of state estimation is proposed. First, the system state equation considering the delay of underwater acoustic communication is established, and the equivalent measurement equation of the system is derived by using the state transfer matrix. Then, the extended Kalman filter (EKF) location method with multiple UUV considering the delay of underwater acoustic communication is presented and its shortcomings are analyzed. Finally, on the basis of EKF method, the effect of measurement information delay on state estimation is analyzed, the real measurement model of the system is established, and the DEKF algorithm based on the minimum mean square error of state estimation is designed. The simulation results show that this method can effectively correct the localization error caused by underwater acoustic communication delay in multi-UUV cooperative location, and it has certain reference significance in engineering practice.
【作者单位】：哈尔滨工程大学自动化学院;
【基金】：国家自然科学基金(51379042,61203225) 中国博士后科学基金面上项目(2012M510083)资助课题
【分类号】：TN929.3;U675.7

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