Nope, as far as I can tell, the schmidt-kalman filter is a way of reducing dimensionality in a KF. You split the states between those you are interested in and

Nope, as far as I can tell, the schmidt-kalman filter is a way of reducing dimensionality in a KF. You split the states between those you are interested in and those you aren't. Sometimes those states you don't care about (e.g. sensor biases) are still important for estimating those you do care about (e.g. position). Adding a state to a typical KF for each of these dimensions is one solution, but it's expensive (covariance calcs scale quadratically with dimension). The Schmidt-Kalman filter is another solution.