A new approach for credibilistic multi-sensor association

Often, when several information sources are available, data are heterogeneous and asynchronous. The combination of all these information sources remains as a difficult task which strongly depends on the representation of the used data. Consequently, it is imperative to choose a model of knowledge representation well adapted to each kind of information. When each source is perfectly represented and modelled, we need to know how to associate them the most faithful and the most reliable way. In this paper, we propose a new credibilistic approach for multi-sensors data association able to resolve the problems mentioned above. This association algorithm provides a reliable and robust representation of an environment by using all available information.

[1]  Véronique Berge-Cherfaoui,et al.  Matching and decision for vehicle tracking in road situation , 1999, Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289).

[2]  Thierry Denoeux,et al.  A k-nearest neighbor classification rule based on Dempster-Shafer theory , 1995, IEEE Trans. Syst. Man Cybern..

[3]  D. Gruyer,et al.  Data association with believe theory , 2000, Proceedings of the Third International Conference on Information Fusion.

[4]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[5]  James Llinas,et al.  Multisensor Data Fusion , 1990 .

[6]  D. Gruyer,et al.  Multi-sensors fusion approach for driver assistance systems , 2001, Proceedings 10th IEEE International Workshop on Robot and Human Interactive Communication. ROMAN 2001 (Cat. No.01TH8591).

[7]  Philippe Smets,et al.  The Combination of Evidence in the Transferable Belief Model , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  D. Gruyer,et al.  Credibilist multi-sensors fusion for the mapping of dynamic environment , 2000, Proceedings of the Third International Conference on Information Fusion.