An Unscented Kalm an Filter-Based MultisensorTrack Fusion Algorithm

Author

Yang, Huijuan ; Zhang, Jian Qiu

Author_Institution

Dept. of Electron. Eng., Fudan Univ., Shanghai

Volume

1

fYear

2005

fDate

16-19 May 2005

Firstpage

527

Lastpage

530

Abstract

In this paper, an unscented Kalman filter (UKF)-based track fusion algorithm is developed for tracking targets in a nonlinear multisensor system. Employing the unscented Kalman filter and the measurements of the individual sensor in the multisensor system, the means and the variances of the states of a tracked target can be estimate. Based on these estimate results, an optimum state fusion scheme is obtained in terms of minimum mean square error (MMSE). The scheme can make the variance of the fused states smaller than that of the states estimated by UKF with any individual sensor in this multisensor system. Simulation results confirm the efficiency of the presented algorithm

Keywords

Kalman filters; least mean squares methods; sensor fusion; target tracking; tracking filters; Kalman filter-based track fusion algorithm; minimum mean square error; multisensor track fusion algorithm; nonlinear multisensor system; optimum state fusion scheme; target tracking; unscented track fusion algorithm; Filters; Multisensor systems; Noise measurement; Random variables; Sensor fusion; Sensor phenomena and characterization; Sensor systems; State estimation; Target tracking; Taylor series;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference, 2005. IMTC 2005. Proceedings of the IEEE

Conference_Location

Ottawa, Ont.

Print_ISBN

0-7803-8879-8

Type

conf

DOI

10.1109/IMTC.2005.1604172

Filename

1604172