Convex optimization approach to the fusion of identity information

Lingjie Li; Zhi-Quan Luo; Kon Max Wong; Eloi Bosse

doi:10.1117/12.341362

12 March 1999 Convex optimization approach to the fusion of identity information

Lingjie Li, Zhi-Quan Luo, Kon Max Wong, Eloi Bosse

Proceedings Volume 3719, Sensor Fusion: Architectures, Algorithms, and Applications III; (1999) https://doi.org/10.1117/12.341362
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

We consider the problem of identity fusion for a multi- sensor target tracking system whereby sensors generate reports on the target identities. Since the sensor reports are typically fuzzy, 'incomplete' and inconsistent, the fusion approach based on the minimization of inconsistencies between the sensor reports by using a convex Quadratic Programming (QP) and linear programming (LP) formulation. In contrast to the Dempster-Shafer's evidential reasoning approach which suffers from exponentially growing completely, our approach is highly efficient. Moreover, our approach is capable of fusing 'ratio type' sensor reports, thus it is more general than the evidential reasoning theory. When the sensor reports are consistent, the solution generated by the new fusion method can be shown to converge to the true probability distribution. Simulation work shows that our method generates reasonable fusion results, and when only 'Subset type' sensor reports are presented, it produces fusion results similar to that obtained via the evidential reasoning theory.

Citation Download Citation

Lingjie Li, Zhi-Quan Luo, Kon Max Wong, and Eloi Bosse "Convex optimization approach to the fusion of identity information", Proc. SPIE 3719, Sensor Fusion: Architectures, Algorithms, and Applications III, (12 March 1999); https://doi.org/10.1117/12.341362

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