Multisensor fusion algorithm for multitarget multibackground classification

Rustom Mamlook; Wiley E. Thompson

doi:10.1117/12.138257

9 July 1992 Multisensor fusion algorithm for multitarget multibackground classification

Rustom Mamlook, Wiley E. Thompson

Proceedings Volume 1699, Signal Processing, Sensor Fusion, and Target Recognition; (1992) https://doi.org/10.1117/12.138257
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

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.

Citation Download Citation

Rustom Mamlook and Wiley E. Thompson "Multisensor fusion algorithm for multitarget multibackground classification", Proc. SPIE 1699, Signal Processing, Sensor Fusion, and Target Recognition, (9 July 1992); https://doi.org/10.1117/12.138257

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