Fuzzy clustering for data fusion in a recognized maritime picture

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Authors
  1. Helleur, C.
Corporate Authors
Defence Research Establishment Ottawa, Ottawa ONT (CAN)
Abstract
The generation and maintenance of a Recognize Maritime Picture consists in part of associating contact reports from sensor sources with existing tracks or initiating new tracks. This decision-making process often takes place in an environment in which the goal and the constraints are not known precisely. To deal quantitatively with imprecision, we usually employ the concepts and techniques of probability theory. The use of a probabilistic approach requires that the imprecision can be equated with randomness and that the characteristics of this randomness are reasonable well known. This is generally not the case for the generation of the RMP. An alternative approach is to view the problem a fuzzy decision making and to employ the concepts and techniques of fuzzy sets. The approach proposed in this paper makes use of the non-real-time nature of the problem to make maximum use of the available data. The proposed approach makes use of the reverse Cuthill-McKee ordering technique to establish an initial estimate of the number of clusters and the c-mean clustering techniques refine the cluster and to establish fuzzy membership functions. The effectiveness of the approach is demonstrated on data acquired off the East Coast of Canada.
Keywords
Data association;Fuzzy logic;HFSWR (High Frequency Surface Wave Radar);RMP (Recognized Maritime Picture);MSDF (Multi-Sensor Data Fusion)
Report Number
DREO-SL-2001-071 — Scientific Literature
Date of publication
01 Aug 2001
Number of Pages
11
DSTKIM No
CA011983
CANDIS No
516582
Format(s):
Hardcopy;Document Image stored on Optical Disk

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