Target Counting with Binary Proximity Sensors Based on Sensor-Cluster Identification

This paper proposes an algorithm for counting the number of distinct targets based on the responses of binary proximity sensors. A basic idea of the proposal is that sensors detecting targets at a specific time should be partitioned into several clusters, where each cluster is composed of sensors detecting a common target. Thus, the number of clusters would be a natural choice of the estimator for the target counting. This paper presents a mathematical framework and a simple algorithm for partitioning a set of target-detecting sensors into several clusters. Simulation experiments verify that the proposed algorithm gives precise estimates of the number of distinct targets.