Research On Bearing-only Target Tracking Method Based On Underwater Passive Detection

Unmanned Underwater Vehicle(UUV)is a kind of underwater unmanned equipment that can be carried by submarines,surface ships,etc.It plays a key role in exploration and development of the ocean.Underwater passive target tracking is a key issue for UUVs to achieve exploration of the ocean.UUVs use passive sonar sensors to detect target azimuths,and use filtering algorithms to track target states.Under the complex marine environment,the detection accuracy of passive sonar will be affected to a certain extent,and the passive sonar obtains less target information,so it is difficult to obtain the state data of the target.Therefore,the research on bearing-only target tracking based on underwater passive detection is of great significance.This paper deeply studies the problems existing in Bearing-Only Target Tracking(BOTT)system,studies the corresponding improved estimation algorithm,and mainly completes the research in the following aspects:1.The detection models of BOTT system and passive sonar are established.By analyzing the characteristics of BOTT system,the sufficient and necessary conditions for the observability of BOTT system are studied,which lays a foundation for the subsequent research on the filtering algorithm of BOTT problem;2.The basic principles,advantages and disadvantages of Extended Kalman Filter(EKF),Pseudo-Linear Kalman Filter(PLKF),Unscented Kalman Filter(UKF)and Cubature Kalman Filter(CKF)are analyzed in detail.Aiming at the BOTT problem,the four filtering algorithms are simulated and verified,and Analyzing the performance of the four algorithms;3.Aiming at the problem that there is a large initial value error in the bott problem,resulting in low filtering accuracy,the idea of forward and reverse filter is introduced to design the initial value optimization method and Forward and Reverse Cubature Kalman Filter(FRCKF)method of BOTT problem to improve the filtering accuracy.Combining the two algorithms to improve the accuracy,the Forward and Reverse Cubature Kalman Filter method considering the initial value optimization is designed.The simulation results show that the Forward and Reverse Cubature Kalman Filter method considering the initial value optimization can improve the filtering accuracy and make the filtering result closer to the real value;4.In view of the abnormal situation of the measured data,the basic principles of chi square detection and Sage-Husa adaptive filter are analyzed in detail.The Cubature Kalman Filter method based on Chi square detection and adaptive volumetric Kalman filter method are designed respectively,and the simulation experiment is carried out.Based on the two algorithms,IGG3 weight function is introduced,and an Adaptive Cubature Kalman Filter method based on innovation correction(IACKF)is proposed and verified by simulation.Finally,considering the target tracking problem of BOTT system,the Innovation Adaptive Forward and Reverse Cubature Kalman Filter(IAFRCKF)method considering initial value optimization is proposed.The simulation results show that the Innovation Adaptive Forward and Reverse Cubature Kalman Filter method considering initial value optimization is more effective in dealing with Bott problem,and the solution result is closer to the real value.