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State-space estimation algorithms and models
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README.md

State-space estimation algorithms

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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.

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