Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual - Stephen Craig Stubberud; Kathleen Ann Kramer; Allen Roger Stubberud (Advances in Science, Technology and Engineering Systems Journal, 2019)
While the Kalman filter, including its many variants, has been the staple of the tracking community, it also has been shown to have drawbacks, particularly when tracking through a maneuver. The most common issue is a lag in the position of the target track compared to the true target position as the target performs its maneuver. Another more problematic issue can occur where the filter covariance collapses, requiring the filter to be reinitialized. Techniques exist to compensate for maneuvers, but generating their response relies on detection of error between the estimated trajectory and the measured target position. In this effort, a maneuver detection routine is developed that can be used in conjunction with more standard maneuver compensation approaches. This routine is able to validate the existence of a maneuver more quickly than use of the inherent detection relied upon in the other methods. Maneuver detection is performed by an evidence accrual system that uses a fuzzy Kalman filter to incorporate new information and provide a level of evidence that maneuver is occurring. The input data uses behavior characteristics of the Kalman gain vector from the tracking algorithm.
Keywords: Evidence accrual; Fuzzy logic; Kalman gain monitoring; Maneuver detection; Pattern detection