Cooperative Target Observation using Density-based Clustering with Self-tuning and a New Grid Environment

摘要

This paper describes and evaluates a Mean-Shift-based (MS) approach to an instance of the Cooperative Target Observation (CTO) problem domain. A performance comparison is presented with a k-means-based approach to the baseline implementation published to the CTO problem. Inspired by the idea of modeling the problem for urban centers in which the movement of targets is restricted to the streets and roads, we also evaluate the effect of the movement of the targets being restricted to a rectangular grid on the relative performance of the algorithms. We conclude that the MS-based approach is superior to the k-means-based approach and that the target motion restricted to a grid improves both algorithms' performance but does not change its relative positions.