An Enhanced Fuzzy logic based Multi-Level Data Association
by
Karim A. Fouad
School of Astronautics, Beijing University of Aeronautics and Astronautics, BUAA
Coauthors: Dr Xu Xiaojian

This paper develops an algorithm to fuse redundant observations due to multiple sensor coverage. Fuzzy membership functions are used as a measure of correlation, and a fuzzy associative system determines which observations represent the same sensors. The result is a computationally efficient algorithm. The output of the system is a unique set of sensors identified by unique platform identifiers. Results of tests based on computer simulation of overlapping radar coverage show that the fusion algorithm correctly correlates and fuses the sensor observations.

This paper presents an algorithm that performs central level fusion on data from various sensor sources providing tracks for display and archival purposes. The algorithm is a refinement of a previously proposed algorithm to fuse the outputs of sensors, such as the Telephonic Remote Site Processor (RSP), providing overlapping coverage. The algorithm has been generalized to accept and fuse an arbitrary number of tracks from any available sensor that can provide any of the following feature information: latitude, longitude, course, and speed. The data collected are fused to create a single unified track table for display for maintenance of an historical record. The fusion process consists of several levels in order to achieve an integrated data set. Also, separate data conversion mechanisms are required to prepare the data for fusion.

Date received: April 28, 2008

Copyright © 2008 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # caxe-00.