Optimization method of maneuvering target state prediction based on Kalman filter

Abstract

The invention discloses a maneuvering target state prediction optimization method based on Kalman filter, which mainly solves the problem of large error of the existing Kalman filter to the target state prediction result. The realization scheme is as follows: setting the parameters of discrete-time linear stochastic dynamic system; According to the set system parameters, the one-step prediction value and gain matrix of Kalman filter system state are calculated. According to the set system parameters, the system state error prediction value and the system measurement error prediction value arecalculated. According to the predicted value of system error, the gain matrix of system error is calculated. According to the gain matrix of system error, the estimation of system state prediction error is calculated, and the system state prediction value is corrected. The invention improves the accuracy of prediction, and enables the prediction result to be closer to the real value of the movingstate of the target, and can be used for predicting the states of the maneuvering targets such as the aircraft, the ship and the automobile.

Description

technical field [0001] The invention belongs to the technical field of communication, in particular to an optimization method for target prediction, which can be applied to the state prediction of maneuvering targets. Background technique [0002] With the rapid development of science and technology, the filtering and prediction of maneuvering targets is a necessary means to estimate the target's motion parameters at present and in the future. This technology has received significant attention in many practical fields, such as: update of aircraft flight trajectory information; fault diagnosis and prediction of industrial systems, etc. The maneuvering target refers to the target in motion, and its movement mode is constantly changing. At present, the methods for predicting the motion state of the maneuvering target include: Kalman filter method, particle filter method, neural network method, etc. Kalman filter is the target tracking and prediction field. The most common meth...

AI Technical Summary

Problems solved by technology

[0003] In the actual target tracking process, the target may lose part of the tracking data continuously due to maneuverability, and the lost data can only be obtained again by predicting the lost data based on the existing data

The Kalman filter can only predict the data one step in advance. If you want to use the Kalman filter for multi-step prediction, because the system measurement value cannot be obtained during the prediction process, the multi-step prediction can only be performed by ignoring the measurement error. As a result, Kalman filter prediction has the disadvantage of low accuracy in multi-step prediction, which leads to the problem of divergence of target prediction results in the process of estimation and prediction

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