Research on multiple AUV cooperative navigation algorithm based on IDDF

Due to limitation of acoustic communication in cooperative navigation problem of multiple autonomous underwater vehicle (AUV), the low update rate will result in large prior estimation errors, poor coordination results and low convergence speed in large initialization errors. In order to improve the performance of AUV cooperative navigation, a algorithm called iterated divided difference filter (IDDF) was proposed. Iterated filtering method and divided difference filter (DDF) algorithm were combined in the algorithm. Compared with traditional nonlinear extended Kalman filter (EKF), the IDDF can not only reduce the influence of system model truncation error to the filtering estimation but also fuse the measurement information adequately by iteratively use in weak observability conditions. Simulation results show that the algorithm can improve the performance of multiple AUV cooperative navigation clearly and the effectiveness and superiority of the algorithm can be verified.