Research On Underwater Target Positioning Techniques Based On Unmanned Moblie Platform Cluster

Cooperative detection and positioning of unmanned platforms is an important part of future underwater operations.This paper takes the unmanned cooperative combat group constructed by unmanned surface vehicle and unmanned underwater vehicle as the scene,and researches on underwater target positioning technology based on unmanned mobile platform cluster,focusing on the position calibration technology of the underwater unmanned mobile platform,underwater acoustic positioning technology of the unmanned cluster and outliers processing technology of target positioning track,and conduct field test verification and data analysis.With regard to the position calibration of underwater unmanned mobile platform,this paper studies the calibration model based on relative distance measurement and the calibration model based on relative distance and orientation measurement,and analyzes the observability of these two models respectively.For nonlinear problem of position calibration,two calibration algorithms based on extended Kalman filter(EKF)and unscented Kalman filter(UKF)are studied.Simulation verification and comparative analysis are carried out on the above research.The research results show that the calibration model based on relative distance and orientation measurement has higher observability,and the performance of UKF algorithm is better than that of EKF algorithm.With regard to the positioning of underwater target in unmanned cluster,the time-of-arrival(TOA)positioning algorithm in the active working mode,the angle-of-arrival(AOA)positioning algorithm and the time difference-of-arrival(TDOA)positioning algorithm in the passive working mode are studied.Aiming at the problem of model mismatch in the active working mode,a TOA optimized model is studied and the calculation method is given;For the mismatch problem of the orientation data or time delay data between the observation platforms and the distance ambiguity problem in the passive working mode,this paper studies the time-space association positioning algorithm of orientation data and the time-space association positioning algorithm of time delay data,and the above algorithms are simulated and verified.With regard to the problem of outliers in target positioning track,the basic principles of common outlier processing methods such as threshold detection method and the detection method based on the statistical characteristics of innovation are introduced;The innovation likelihood method based on Kalman filtering is studied to detect,remove and correct outliers.The simulation results show that the innovation likelihood method has a better performance.In order to verify the performance of the above algorithms,field tests and experimental data processing are carried out.The data processing results verify the feasibility and effectiveness of these algorithms.