Research On Multi USV Cooperative Positioning Data Fusion Technology

With the increasing of marine missions' risk and complexity,single unmanned surface ships have been unable to accomplish these tasks.Therefore,many scholars began to study the cooperative localization of multiple unmanned surface ships.During communication,many unmanned sur face ships need to communicate and exchange information,so as to reduce their positioning errors and improve the accuracy of the cooperative positioning system.As the application background of the collaborative system in this paper is a strong interference environment,the communication mode between unmanned surface boats is selected with good anti-jamming acoustic communication.However,the sound signals need a certain time to transmit in the water,and there are many uncertainties in the operational environment,which can't be seen as immediate arrival.This leads to the existence of some information when data fusion is delayed.Therefore,it is necessary to design an effective data fusion algorithm to reduce the communication delay to the performance o f the system.Firstly,considering the characteristics of the multi unmanned surface craft cooperative system,the characteristics and error sources of the sensor are analyzed,and the motion equations and measurement equations of the system are establishe d by selecting suitable sensors and communication methods.Then,the observability of different cooperative positioning methods is analyzed,then the covariance upper bound of different cooperative modes is analyzed based on extended Kalman filter,and its performance is compared and analyzed,and the cooperative positioning mode of the two main one from this paper is determined,and then the performance of the system is verified by simulation.In this paper,the delay extended Kalman filtering algorithm is used to solve the influence of communication delay on the system accuracy.The basic principle is to introduce the time delay into the equation of state and the measurement equation,Restructure the system equation,and then carry out the state inversion.This method will be invalidated when the communication information is lost,and the computation is large and the storage space needed is large.In order to solve this problem,this paper proposes an algorithm for estimating the absolute position track,which can be used to calculate the track of the absolute position information by modeling,and to fuse the data measured from the unmanned surface boat's own sensor,so that the system information can be updated in real time when the communication delay and packet loss exist.Finally,different cooperative location methods are simulated to build vehicle cooperative positioning system,and the difference of system performance under different cooperative modes is analyzed.Then,the vehicle cooperative experime nt is simulated in a communication limited environment to verify the effectiveness and analysis of the data fusion algorithm of the two filters.The experimental results show that the positioning error can converge to a smaller value when there is absolute position information in the cooperative positioning system.At the same time,the effectiveness of the two data fusion algorithms can be verified,which can reduce the influence of communication constraints on the positioning accuracy of the system and improve the performance of the cooperative positioning system.