Research On Processing Method For Exterior Ballistic Tracking Velocity Measurement Data

In this paper,taking the data processing process of external measurement as the clue,taking the data of external trajectory tracking and velocity measurement as the main research object,some new post-processing methods of external trajectory tracking and velocity measurement data are studied.The related research contents include the following parts:(1)Aiming at the problem of outlier detection and feature point detection in velocity measurement data of external ballistic tracking(velocity measurement elements),this paper studies and proposes outlier detection(correction)method based on RANSAC algorithm and feature point detection algorithm based on resampling and neighborhood clustering,and gives two implementation methods for each algorithm,Simulation results show that the two algorithms can effectively detect outliers and feature points in the velocity measurement data of exterior trajectory tracking.(2)In this paper,the self localization technology of one master and multiple slave tracking velocity measurement in multi velocity measurement system is studied,which makes full use of high-precision measurement data and gets rid of the dependence on low precision positioning parameters to realize the self positioning of aircraft trajectory parameters.(3)This paper studies and proposes a data-driven linear unbiased minimum variance estimation(LUMV)trajectory parameter fusion method,which can mine the dynamic time-varying accuracy of measurement data by using the sliding least aquares fitting residual method,and realize the trajectory parameter fusion based on the optimal weight of linear unbiased minimum variance estimation.The simulation experiment results show that the fusion accuracy of the new algorithm is significantly improved.(4)Aiming at the problem of optimal estimation of trajectory parameters,this paper studies and proposes the optimal SG smoothing algorithm and the optimal cubic smoothing spline smoothing algorithm based on variable difference method.The two algorithms can find the optimal model parameter estimation value through iterative method based on their own data characteristics,which provides a new solution for optimao estimation of trajectory parameters.(5)In this paper,we use python programming language to develop a supporting data processing software system of trajectory tracking and velocity measurement,and embed all the algorithms studied and proposed in this paper into the software system.To a certain extent,the software system realizes the automation of the data processing process of trajectory tracking and velocity measurement,and improves the efficiency of data processing.