State-space estimation algorithms such as Kalman Filters.
The goal of this library is to simplify implementation and use of state-space estimation algorithms, such as Kalman Filters. The interface for constructing models is isolated as much as possible from the specifics of a given algorithm, so swapping out a Kalman Filter for a Bayesian Particle Filter should involve a minimum of effort.
This implementation is designed to support symbolic types, such as from sbv or ivory. As a result you can generate code in another language, such as C, from a model written using this package; or run static analyses on your model.
Also included is a sophisticated sensor fusion example in Numeric.Estimator.Model.SensorFusion, which may be useful in its own right.