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

Content Provider	IET Digital Library
Author	Li, Shuhui Feng, Xiaoxue Deng, Zhihong Pan, Feng Ge, Shengyang
Abstract	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.
Starting Page	199
Ending Page	213
Page Count	15
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 4, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/4
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2019.0178
Journal	IET Signal Processing
Publisher Date	2020-02-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Entropy Filtering Method in Signal Processing Filtering Theory General Systematic Bias Evolution Model Indoor Target Tracking Interpolation And Function Approximation Kalman Filter Minimum Error Entropy Minimum Variance Unbiased Filter Multiple Model Estimation Multiple Model Observer Multiple System Uncertainties Multiple Uncertainties Multisensor Hybrid Target Multisensor Hybrid Uncertain Target Tracking System Multisensor Target Tracking System Numerical Analysis Observers Optimal State Estimation Optimal Systematic Biases Optimisation Technique Positioning Experiment Sensor Fusion State Estimation State Estimation Accuracy Statistics System Measurement Target Tracking Target Tracking Performance Target Tracking Scenario Unknown State
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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