Design of Schmidt-Kalman filter for target tracking with navigation errors

In most target tracking formulations, the tracking sensor location is typically assumed perfectly known. Without accounting for navigation errors of the sensor platform, regular Kalman filters tend to be optimistic (i.e., the covariance matrix far below the actual mean squared errors). In this paper, the Schmidt-Kalman filter (SKF) is formulated for target tracking with navigation errors. The SKF does not estimate the navigation errors explicitly but rather takes into account (i.e., considers) the navigation error covariance provided by an on-board navigation unit in the tracking filter formulation. Including the navigation errors leads to the so-called “consider covariance.” By exploring the structural navigation errors, the SKF is not only more consistent but also produces smaller mean squared errors than regular Kalman filters. Monte Carlo simulation results are presented in the paper to demonstrate the operation and performance of the SKF for target tracking in the presence of navigation errors.1,2