<title>Sensor fusion using K-nearest neighbor concepts</title>

Abstract

A new K-nearest neighbor (KNN) statistic is introduced to fuse information from multiple sensors/features into a single dimensional decision space for electronic vision systems. Theorems establish the relationship of the KNN statistic to other probability density function distance measures such as the Kolmogorov-Smirnov Distance and the Tie Statistic. A new KNN search algorithm is presented along with factors for selecting K. Applications include cueing and texture recognition.

Developing a Methodology to Improve the Performance of Smart Weapon Systems in Countermeasure Environments

“…Fast algorithms [7] have been developed to locate the k_nearest neighbors in large dimensional feature spaces. The number of comparisons required to locate the k_nearest neighbors is linear with the n th root of the number of samples in the database where n is the dimension of the feature space.…”