Consistent Extended Kalman Filter-Based Cooperative Localization of Multiple Autonomous Underwater Vehicles

Abstract

In order to solve the problem of inconsistent state estimation when multiple autonomous underwater vehicles (AUVs) are co-located, this paper proposes a method of multi-AUV co-location based on the consistent extended Kalman filter (EKF). Firstly, the dynamic model of cooperative positioning system follower AUV under two leaders alternately transmitting navigation information is established. Secondly, the observability of the standard linearization estimator based on the lead-follower multi-AUV cooperative positioning system is analyzed by comparing the subspace of the observable matrix of state estimation with that of an ideal observable matrix, it can be concluded that the estimation of state by standard EKF is inconsistent. Finally, aiming at the problem of inconsistent state estimation, a consistent EKF multi-AUV cooperative localization algorithm is designed. The algorithm corrects the linearized measurement values in the Jacobian matrix for cooperative positioning, ensuring that the linearized estimator can obtain accurate measurement values. The positioning results of the follower AUV under dead reckoning, standard EKF, and consistent EKF algorithms are simulated, analyzed, and compared with the real trajectory of the following AUV. The simulation results show that the follower AUV with a consistent EKF algorithm can keep synchronization with the leader AUV more stably.

Autonomous Underwater Vehicle Navigation via Sensors Maximum-Ratio Combining in Absence of Bearing Angle Data

“…The problem is considered in the 2D plane [29] The algorithm proposed in this paper adds the zero-space vector as the observability constraint, which improves the consistency of the cooperative positioning system…”

“…The model of a joint AUV positioning system is often nonlinear. Therefore, the extended Kalman filter (EKF) [28][29][30] and the uncentered Kalman filter (UKF) [31,32] are commonly used for state estimation. The accuracy of both EKF and UKF is affected by various factors, such as filter model and noise characteristics.…”

“…The expressions presented in [38] were used to simulate the measurements of each sensor in the navigation system. The extended Kalman filter for state filtering was formed according to the general methodology presented in a number of sources [26][27][28][29][30]. The well-known maximum-ratio combining method was used as the considered method of sensor fusion.…”

Optimal Configuration of Heterogeneous Swarm for Cooperative Detection with Minimum DOP Based on Nested Cones

“…The sensor configuration optimization theory based on Fisher information is relatively late in the development of unmanned swarms. Zhang studied the positioning performance of a leader-slave multi-AUV (autonomous underwater vehicle) cooperative positioning system and analyzed the influence of different configurations on the positioning performance through simulation [15]. Fang proposed an optimal configuration estimation method for cooperative positioning based on distance measurement and verified that the optimal formation could obtain better positioning accuracy by analyzing the configurations of single-lead underwater vehicles and double-lead underwater vehicles [16].…”

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

“…The other aspect is to improve the processing methods of INS and GNSS data. Specifically, it can be divided into improving the data fusion algorithm [17] and using artificial intelligence technology [18].…”