A nonlinear prediction filter algorithm based on the adaptive tracking theory

Mao-tao Xiong; Qin-zhang Wu; Xiao-dong Gao

doi:10.1117/12.765765

22 January 2008 A nonlinear prediction filter algorithm based on the adaptive tracking theory

Mao-tao Xiong, Qin-zhang Wu, Xiao-dong Gao

Proceedings Volume 6833, Electronic Imaging and Multimedia Technology V; 68330K (2008) https://doi.org/10.1117/12.765765
Event: Photonics Asia 2007, 2007, Beijing, China

Abstract

The tracking and orientation of optoelectronic targets must obtain the data of target's velocity and angle by prediction algorithm. But the state and measurement equations are usually nonlinear and uncoupled models, so the tracking problem often connects with nonlinear estimation. The commonly classical extended Kalman filter (EKF) algorithm suffers from a lot of defects. There are those problems such as easy to diverge and the convergence rate is slow and the tracking accuracy is low. In this paper, a new nonlinear adaptive Kalman filter (AEKF) algorithm based on the adaptive tracking theory in current statistical model is presented. It expresses variation of acceleration with the information of position and angle to carry out self adaptation of noise variance in on-line mode, and to compensate the linear errors of model in dynamic mode. Analytic results of Monte Carlo simulation prove the AEKF algorithm is right and feasible, and the accuracy and the convergence rate are both improved. It has better performance than the EKF algorithm and modified variance EKF (MVEKF) algorithm in the tracking and orientation of optoelectronic maneuvering target. The simulation results and new method will been widely and directly applied into various engineering.

Citation Download Citation

Mao-tao Xiong, Qin-zhang Wu, and Xiao-dong Gao "A nonlinear prediction filter algorithm based on the adaptive tracking theory", Proc. SPIE 6833, Electronic Imaging and Multimedia Technology V, 68330K (22 January 2008); https://doi.org/10.1117/12.765765

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available