Research On Cooperative Navigation Information Fusion For Multiple Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUV) as a multiplier of marine exploration, could implement scientific investigation and military activities in great depths hazardous areas,which is humans can not reached, received widespread attention. The multi-AUV collaboration technology developed on the basis of AUV has been more and more attention,and as the basis of collaboration technology, Cooperative navigation has become the focus of attention. Compared with a single AUV,multi-AUV Cooperative navigation has a unique advantage in reducing the system configuration of AUV, enhancing information share between AUV and improving navigation accuracy. Considered the actual needs of improve multi-AUV Cooperative navigation performance, researched focus on multi-AUV Cooperative navigation algorithm based on moving radius vector, the study includes the following:The pilot AUV integrated navigation positioning methods of multi-AUV Cooperative navigation are studied, included SINS and Doppler Log two navigation devices and their navigation solution. In view of integrated navigation output data exist the effect of random perturbations, to weaken the influence using gray prediction model add to the irregular original data calculated of integrated navigation. By means of surface ships beacon combines the main and follow AUV. Analysis the structure of the model geometry with each other,establish the overall positioning algorithm based on tetrahedral structure. And considered the links of each other between the main AUV located through integrated navigation system, and the follow AUV navigation through Cooperative navigation system, judging the rationality and accuracy of the navigation and positioning.The main means of communication underwater acoustic communication system are stduied, compared the difference kind of underwater acoustic communication networks. Then for a single leader positioning structure, Considered multi-AUV Cooperative navigation technology based on moving radius vector, built a mathematical model of collaborative navigation. Because of the single distance measurement of the system, it's lead to poor of Observability. Using nonlinear system LEE derivative theory analysis the observability of the system, combined the system observability conditions, clear the observed motion path of the system.An improved unscented Kalman filter estimation algorithm about measurement noise adaptive estimation is proposed. First on the basis of unscented Kalman filter, to resolve measurement noise uncertainty which is caused by communication delays and communication packet loss problems in the process of underwater communication,applied the maximum a posterior (MAP) estimation theory to design a time-varying noise statistic estimator of noise mean and covariance estimates, Then,and correct the estimator through exponential weighting. Finally, simulated analysis of the Collaborative Navigation System is done, it is proved that adaptive UKF algorithm has a good filtering effect for Collaborative Navigation System with variable noise statistic, the accuracy and efficiency have been improved remarkably.For model uncertainties exist in the actual system, consider UKF similar to Extended Kalman Filter (EKF) system cannot overcome the model uncertainty problem of robustness.Using strong tracking filter amended the filtering algorithms, consider internal links between UKF and EKF, designed strong tracking filter which is suited to UKF. updated for the modified collaborative navigation system based on strong tracking adaptive unscented Kalman filter, explained the situation when system input exists the presence of disturbances and model uncertainty, strong tracking filter good way to improve the system of robustness.