Multiple - Sensor Fusion Target Tracking using ClusterTrack Algorithm

Despite the minimal information provided by a binary proximity sensor, a network of these sensors can provide significant target tracking performance. This article deals with the performance examination of such a network for tracking multiple targets. We began with geometric arguments that address the problem of counting the number of distinct targets, given a snapshot of the sensor readings. Then necessary and sufficient criteria provided for an accurate target count in a one-dimensional setting, moreover, a greedy algorithm defined that determines the minimum number of targets that is consistent with the sensor readings. While these combinatorial arguments bring out the difficulty of target counting based on sensor readings at a given time, they leave open the possibility of accurate counting and tracking by exploiting the evolution of the sensor readings over time. To this end, we develop a particle filtering algorithm based on a cost function that penalizes changes in velocity. Finally, an extensive set of simulations, as well as experiments with passive infrared sensors, are reported.