Reduced order decomposition for steady state biased Kalman filters

The problem of estimating the state x of a linear system in the presence of a constant, but unknown bias vector b is considered. Applying results derived for optimal filtering of singularly perturbed systems, the reduced order filters for state and bias are obtained. The presented approach completely decouples state and bias filters, both of them being driven by the systems measurements, thus allowing parallel computations.