Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

Shuhui Li, Xiaoxue Feng*, Zhihong Deng, Feng Pan, Shengyang Ge

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

6 引用 (Scopus)

摘要

In the multisensor target tracking system, the key of the target tracking performance depends on the state estimation accuracy to a great extent. However, the system uncertainties will seriously affect the performance of the state estimation. Up to now, little research focuses on the state estimation for the multi-sensor hybrid target tracking systems with multiple uncertainties including the multiple models, the unknown inputs and the systematic biases. In this study, the minimum error entropy based on the multiple model estimation for the multisensor hybrid uncertain target tracking systems with the multiple system uncertainties is presented. The minimum variance unbiased filter based on the general systematic bias evolution model decoupled with the unknown state is designed to estimate the optimal systematic biases and compensate the system measurements. Taking full advantage of the compensated measurement information in time and space, the multiple model observer based on the minimum error entropy is designed to obtain the optimal state estimation. The simulation results of the target tracking scenario illustrate the effectiveness of the proposed method, and the indoor target tracking and positioning experiment based on the ultrawideband further verifies that the proposed method is satisfying.

源语言英语
页(从-至)199-213
页数15
期刊IET Signal Processing
14
4
DOI
出版状态已出版 - 1 6月 2020

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引用此

Li, S., Feng, X., Deng, Z., Pan, F., & Ge, S. (2020). Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems. IET Signal Processing, 14(4), 199-213. https://doi.org/10.1049/iet-spr.2019.0178