A multisensor fusion algorithm to classify the inputs (data or images) into classes (targets, backgrounds) is presented. The algorithm forms clusters and is trained without supervision. The clustering is done on the basis of the statistical properties of the set of inputs. This algorithm implements a clustering algorithm that is very similar to the simple sequential leader clustering algorithm and the Carpenter/Grossberg net algorithm (CGNA). The algorithm differs from CGNA in that (1) the data inputs and data pointers may take on real values, (2) it features an adaptive mechanism for selecting the number of clusters, and (3) it features an adaptive threshold. The problem of threshold selection is considered and the convergence of the algorithm is shown. An example is given to show the application of the algorithm to multisensor fusion for classifying targets and backgrounds.

Date Published: 9 July 1992
PDF: 11 pages
Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992);

Published in SPIE Proceedings Vol. 1699:
Signal Processing, Sensor Fusion, and Target Recognition
Vibeke Libby; Ivan Kadar, Editor(s)