A Self-Organizing Multisensor Fusion Classification Algorithm

Abstract

A self-organizing 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. The algorithm is a self-organizing algorithm, since it has the ability to form and adjust the number of clusters without being given the correct number of clusters. 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 algorithm does not require the number of classes been known apriori. 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 for multisensor fusion for classifying targets and backgrounds, and the results of using this algorithm is compared to the results of using K-nearest neighbor algorithm.