Real time target tracking with binary sensor networks and parallel computing

Abstract

A parallel real time data fusion and target tracking algorithm for very large binary sensor networks is presented. A binary sensor can give an on or off signal to indicate the presence or absence of targets within its range, but it cannot tell how many targets are present, where the targets are, how fast they are moving, or which direction they are heading. In order to detect and track targets using these sensors, it is necessary to fuse information from more than one sensor. A parallel data fusion process based on simulated annealing is used to identify and locate targets. Processing is performed on a commodity Linux cluster with communication between nodes facilitated by the Message Passing Interface (MPI). The fusion and tracking algorithm is tested with a wide variety of sensor network parameters using target track data from a theater level air combat simulation. It is demonstrated that very accurate target detection and localization are possible even though the binary sensors themselves provide little information and have high error rates. Real time tracking is performed on a network with 2.5 million sensors on a commodity cluster with only 50 processors.