| Issue |
MATEC Web Conf.
Volume 176, 2018
2018 6th International Forum on Industrial Design (IFID 2018)
|
|
|---|---|---|
| Article Number | 01017 | |
| Number of page(s) | 5 | |
| Section | Intelligent Design and Computer Technology | |
| DOI | https://doi.org/10.1051/matecconf/201817601017 | |
| Published online | 02 July 2018 | |
Generalized Labeled Multi-Bernoulli Extended Target Tracking Based on Gaussian Process Regression
School of Information and Navigation, Air Force Engineering University, Xi’an, China
Corresponding author: 592255820@qq.com
Corresponding author: fengxinxi2005@aliyun.com
Corresponding author: 1766584727@qq.com
For the problems that Gamma Gaussian Inverse Wishart Cardinalized Probability Hypothesis Density (GGIW-CPHD) filter cannot accurately estimate the extended target shape and has a bad tracking performance under the condition of low SNR, a new generalized labeled multi-Bernoulli algorithm based on Gaussian process regression is proposed. The algorithm adopts the star convex to model the extended target, and realizes the online learning of the Gaussian process by constructing the state space model to complete the estimation of the extended target shape. At the same time, in the low SNR environment, the target motion state is tracked by the good tracking performance of the generalized label Bernoulli filter. Simulation results show that for any target with unknown shape, the proposed algorithm can well offer its extended shape and in the low SNR environment it can greatly improve the accuracy and stability of target tracking.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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