Title :
An Unscented Kalm an Filter-Based MultisensorTrack Fusion Algorithm
Author :
Yang, Huijuan ; Zhang, Jian Qiu
Author_Institution :
Dept. of Electron. Eng., Fudan Univ., Shanghai
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;
Conference_Titel :
Instrumentation and Measurement Technology Conference, 2005. IMTC 2005. Proceedings of the IEEE
Conference_Location :
Ottawa, Ont.
Print_ISBN :
0-7803-8879-8
DOI :
10.1109/IMTC.2005.1604172
