Nonlinear moving target tracking based on LightGBM model

Changcheng Wang; Fan Yang; Kan Zeng; Peng Fan; Lisi Chen

doi:10.1117/12.3029170

6 May 2024 Nonlinear moving target tracking based on LightGBM model

Changcheng Wang, Fan Yang, Kan Zeng, Peng Fan, Lisi Chen

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 131073T (2024) https://doi.org/10.1117/12.3029170
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

To enhance the capability of assessing and forecasting the spatial-temporal situation of nonlinear moving targets, a nonlinear moving target tracking method based on LightGBM gradient boosting decision tree is proposed. The LightGBM model is used to learn the features of the target trajectory, establish the mapping relationship between the incremental target trajectory motion and the input features, and realize the high accuracy forecast of the nonlinear motion target trajectory. The experimental results show that the method is significantly better than the Kalman filter model in terms of prediction accuracy and prediction robustness by predicting the future points of the trajectory of a typical precision-guided weapon guided bomb and comparing the prediction results with the Kalman filter model. On each track of the test set, the mean absolute errors of the predictions of the LightGBM model in azimuth, altitude and oblique distance are 4.27%, 4.33% and 5.43% of the predictions of the Kalman filter model, respectively, and the mean square deviation are 4.11%, 4.24% and 5.21% of the predictions of the Kalman filter model. The importance of the features is analyzed by the SHAP method, and it is found that the derived features of target trajectory motion increments predicted by Kalman filter model contributed most to the model forecasts.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Changcheng Wang, Fan Yang, Kan Zeng, Peng Fan, and Lisi Chen "Nonlinear moving target tracking based on LightGBM model", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 131073T (6 May 2024); https://doi.org/10.1117/12.3029170

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Signal filtering

Tunable filters

Motion models

Electronic filtering

Electrooptical modeling

Decision trees

Histograms

Show All Keywords

Keywords/Phrases

Search In:

Publication Years