基于水声通信延迟的多UUV协同定位算法

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。

Cooperative location of multiple UUVs based on hydro-acoustic communication delay

Considering the positioning failure caused by the communication delay during cooperative location of multiple unmanned underwater vehicles (UUV), a delayed extended Kalman filter (DEKF) method for localization error correction is proposed which is based on least mean square error of state estimation. Firstly, the system state equation with hydro-acoustic communication delay is established, and the measure equation of multiple UUVs is derived using the system state transition matrix. Then we introduce the extended Kalman filter (EKF) localization algorithm for cooperative location of multiple UUVs considering hydro-acoustic communication delay, and analyze the disadvantages of this method. Finally, on the basis of EKF, through analyzing the influence of measure information delay to state estimation, the equivalent measurement model is established and the DEKF algorithm based on least mean square error of state estimation is designed. The simulation results show that this method can effectively correct localization errors caused by communication delay during the cooperative location, which is of great significance in engineering practice.