Minimum Origin Uncertainty State Estimation ( MOUSE ) Algorithm Performance Bounds March 1998

This preliminary report focuses on the performance of the Probabilistic Multi-hypothesis Tracking (PMHT) algorithm as a precursor to a more thorough examination of the MOUSE track fusion algorithm. The rationale for this decision is based on the close similarity between the original PMHT technique as advanced by Streit and Luginbuhl and the MOUSE track fusion concept. In particular, both algorithms use soft probabilistic assignments of measurements-totracks based on the Expectation Maximization (EM) algorithm as formalized by Dempster and, therefore, are more computationally efficient than the vast majority of hard assignment approaches. The core tracking algorithms are also similar; for linear process models and measurements, both algorithms use a fixed-interval batch Kalman filter. Therefore, developing performance bounds for the assignment probabilities and target states appears to be an appropriate way of easing into the more difficult problem of track fusion. The basic building blocks for the MOUSE analysis procedure are completed and are addressed in this report. However, issues pertinent to track fusion and track initiation in clutter remain to be completed. This will be addressed in a subsequent report.