DocumentCode :
1496399
Title :
Convex optimization approach to identify fusion for multisensor target tracking
Author :
Li, Lingjie ; Luo, Zhi-Quan ; Wong, K. Max ; Bossé, Eloi
Author_Institution :
Dept. of Electr. & Comput. Eng., McMaster Univ., Hamilton, Ont., Canada
Volume :
31
Issue :
3
fYear :
2001
fDate :
5/1/2001 12:00:00 AM
Firstpage :
172
Lastpage :
178
Abstract :
We consider the problem of identity fusion for a multisensor target tracking system whereby the sensors generate reports on the target identities. Since sensor reports are typically fuzzy, incomplete, or inconsistent, the fusion of such sensor reports becomes a major challenge. In this paper, we introduce a new identity fusion method based on the minimization of inconsistencies among the sensor reports by using a convex quadratic programming formulation. In contrast to Dempster-Shafer´s evidential reasoning approach which suffers from exponentially growing complexity, our approach is highly efficient (polynomial time solvable). Moreover, our approach can fuse sensor reports of the form more general than that allowed by the evidential reasoning theory. Simulation results show that our method generates reasonable fusion results which are similar to that obtained via the evidential reasoning theory
Keywords :
computational complexity; convex programming; probability; quadratic programming; sensor fusion; target tracking; computational complexity; convex optimization; convex programming; decision fusion; multisensor target tracking; polynomial time; probability; quadratic programming; sensor fusion; Aircraft; Bayesian methods; Fuses; Fusion power generation; Minimization methods; Polynomials; Quadratic programming; Sensor fusion; Sensor systems; Target tracking;
fLanguage :
English
Journal_Title :
Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4427
Type :
jour
DOI :
10.1109/3468.925656
Filename :
925656
Link To Document :
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