About the significance of the Schmidt-Kalman filter (from a related abstract): >In most target tracking formulations, the tracking sensor location is typically

About the significance of the Schmidt-Kalman filter (from a related abstract): >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) ... The Schmidt-Kalman filter (SKF) does not estimate the navigation errors explicitly but rather takes into account the navigation error covariance provided by an on-board navigation unit in the tracking filter formulation. By exploring the structural navigation errors, the SKF is not only more consistent but also produces smaller mean squared errors than regular Kalman filters.