Research On Multi-AUV Cooperative Navigation Method Based On Improved CKF Algorithm

With the gradual shift of human attention from land to ocean,the importance of underwater resource exploration and development has become increasingly prominent.Autonomous underwater vehicle(AUV)can perform various underwater operations at ocean depths or dangerous areas that are not accessible to human beings.Multi-AUV cooperative navigation technology with many advantages has gradually become the main direction of AUV development.Aiming at the problems of positioning accuracy,abnormal situation and navigation efficiency in cooperative navigation,this paper improves the multi-AUV cooperative navigation algorithm with the goal of improving the accuracy,stability and efficiency of cooperative navigation.The main contents are as follows:In order to improve positioning accuracy,handle anomalies and optimize navigation efficiency,three multi-AUV cooperative navigation algorithms based on improved cubature Kalman filter(CKF)are presented.Firstly,after analyzing two kinds of multi-AUV cooperative navigation motion models and establishing an appropriate system state space model,the state variable is expanded to overcome the problem of multiplicative noise propagation,while the embedded cubature criterion is used to improve the traditional CKF.And a multi-AUV cooperative navigation method based on the augment embedded cubature Kalman filter is proposed to achieve accurate positioning of the following AUV in higher system dimensions.Then,aiming at the problem of navigation accuracy reduction caused by time-varying non-Gaussian measurement noise,the augment embedded cubature Kalman filter is improved based on the Sage-husa adaptive criterion,and a multi-AUV cooperative navigation method based on the augment adaptive embedded cubature Kalman filter is proposed to estimate and correct the varying measurement noise in real-time.Finally,in order to solve the problem that the algorithm fails when the model of multi-AUV cooperative navigation system is uncertain or the system state changes abruptly,the strong tracking filter is introduced into the augment adaptive embedded cubature Kalman filter,while a system convergence criterion is added.By selecting a strong tracking filter or an adaptive algorithm based on the criterion,a multi-AUV cooperative navigation method based on the strong tracking adaptive embedded cubature Kalman filter is presented to improve cooperative navigation performance while improving positioning efficiency.According to the design of the cooperative navigation algorithms in different situations,the multi-AUV cooperative navigation system model and different simulation scenes are established to validate the improved cooperative navigation methods,and the navigation results of the improved CKF-based cooperative navigation algorithms proposed in this paper and the traditional cooperative navigation algorithms are compared.The simulation results show that the proposed cooperative navigation algorithm based on improved CKF can better cope with the sudden change of AUV motion state and unknown time-varying non-Gaussian measurement noise under complex tasks.It can maintain high AUV navigation accuracy and algorithm stability with large navigation time,and has high navigation efficiency to meet the requirements of real-time system.