[1] A common application is to account for the effects of nuisance parameters such as sensor biases without increasing the dimensionality of the state estimate.
This ensures that the covariance matrix will accurately represent the distribution of the errors.
The primary advantage of utilizing the Schmidt–Kalman filter instead of increasing the dimensionality of the state space is the reduction in computational complexity.
For use in non-linear systems, the observation and state transition models may be linearized around the current mean and covariance estimate in a method analogous to the extended Kalman filter.
Stanley F. Schmidt developed the Schmidt–Kalman filter as a method to account for unobservable biases while maintaining the low dimensionality required for implementation in real time systems.