A multistage tracker for distributed sensor fields

This paper presents the results of using a multiple stage tracker to improve tracking results on a multistatic sonar dataset. The tracker consists of a predetection fusion step, an extended Kalman filter implementation of joint probabilistic data association (JPDA), and a Monte Carlo implementation of JPDA. The predetection fusion step, combined with the first EKF-JPDA step is used to detect targets and initialize tracks in the Monte Carlo JPDA tracker. The Monte Carlo JPDA tracker allows for the use of multiple models, as well as accurately modeling the measurement uncertainty without linearity approximations. The multiple stage tracking system results in improved localization and decreased fragmentation when compared to the baseline tracker.