Design And Algorithm Research Of Autonomous Underwater Vehicle Positioning System

As the autonomous underwater vehicle(AUV)is abroad used in many fields and the underwater positioning system is the key to ensure the normal work of AUV,the design of underwater target positioning system and the research of the positioning algorithm are very important.The main work of this paper is to complete the design of underwater positioning system and the research of the time delay estimation algorithm and positioning algorithm.(1)The positioning system is based on the short-baseline principle,combined with three hydrophones,an ultrasonic transducer,and a water depth meter to compose an improved short-baseline array,which is used to locate the underwater target.This system has the advantages of high positioning accuracy,easy operation and simple installation.(2)This paper analyses the influence of noise,sampling rate and multipath effect on the time delay estimation results,summarizes the method for improving precision of time delay estimation through the concrete derivation,and proposes a new time delay estimation and peak detection algorithm.By combining this algorithm with the method of correcting the underwater sound velocity and sound line,the range information can be obtained accurately.The validity and feasibility of the method have been verified by experiments.(3)This paper compares the performance of the artificial intelligence algorithm and the Kalman filter algorithm for the underwater target positioning.The artificial intelligent algorithm includes genetic algorithm(GA),adaptive genetic algorithm(AGA)and artificial bee colony algorithm(ABC).The Kalman filtering algorithm includes extended Kalman(EKF)and unscented Kalman algorithm(UKF).The basic theory and process of these algorithms are described,and the advantages and disadvantages of these algorithms are analyzed theoretically in this paper.The experimental results show that the positioning accuracy of artificial bee colony algorithm is the highest,but the ABC algorithm cannot be used for real-time positioning;the positioning accuracy of the extended Kalman filter algorithm and the unscented Kalman filter algorithm is lower,but they can be used for the real-time tracking of underwater targets.Therefore,in order to further improve the positioning accuracy and ensure the real-time positioning,a combination of Kalman filtering algorithm and artificial intelligence algorithm is proposed in the paper.Combined the two Kalman filtering algorithms and adaptive genetic algorithm and artificial bee colony algorithm respectively to solve the positioning problem.The experimental results show that the hybrid algorithm has the highest accuracy and good performance.(4)The influence of the depth measurement error,the measurement error of underwater acoustic distance and the structure of the short baseline array on the positioning results are analyzed by Matlab simulation in this paper and the optimal short baseline array structure is proposed.