Towards a computationally efficient approach for improving target tracking using grid-based methods

摘要

Although the Kalman filter is efficient and effective for computing state estimates of a moving target, it can produce poor results when tracking a maneuvering target. The problem is that the Kalman filter must employ large plant noise and/or large tracking gates to keep the target in track. This can result in larger errors in the state estimate as well as larger uncertainties in these estimates. To track these maneuvering targets, a better approach would be to exploit the kinematic constraints of the target to restrict the state estimates to only those where the target transition was possible. Unfortunately, the Kalman filter cannot fully capture the physical constraints of the target motion. To address this problem, several alternative approaches have been pursued including Kalman filter variants, particle filters, and gridbased filters. Although grid-based filters can be effective, it seems they have been avoided due to their perceived exponential computational requirements. A new approach for using a grid-based filter has been developed that can track targets moving in two dimensions by using a well-confined, two-dimensional grid. As a result, this grid-based approach is enormously more computationally efficient and can effectively exploit the kinematic constraints of the target. This paper describes this grid-based filter, along with the inclusion of the kinematically-constrained target motion model. The paper will then compare the tracking performance of this filter against a Kalman filter for maneuvering target scenarios. The improved target state estimations from this grid-based filter will be shown and analyzed via Monte Carlo analysis