CIFlt.hpp

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#ifndef _BAYES_FILTER_CI
#define _BAYES_FILTER_CI

/*
 * Bayes++ the Bayesian Filtering Library
 * Copyright (c) 2002 Michael Stevens
 * See accompanying Bayes++.htm for terms and conditions of use.
 *
 * $Id$
 */

/*
 * Covariance Intersection Filter Scheme.
 *
 * References
 *  [1] "A Non divergent Estimation Algorithm in the Presence of Unknown Correlations"
 *   Simon J Julier, Jeffrey K Uhlmann
 *
 * CI provides a generalised consistent method to combine mean and covariances
 * of two estimates. The combination can be optimised by choosing a norm of the
 * combined correlations. The norm (omega) is restrict to 0..1 inclusive an effectively
 * scales the combination
 * Here is CI with a predict and observe model to form a filter.
 *
 * The Omega norm chosen here is the fixed value of 0.5
 * The Omega function should be overloaded to produce more useful results
 *
 * The filter is operated by performing a
 *  predict, observe
 * cycle derived from the Extended_filter
 */
#include "bayesFlt.hpp"

/* Filter namespace */
namespace Bayesian_filter
{

class CI_scheme : public Extended_kalman_filter
{
public:
    CI_scheme (std::size_t x_size, std::size_t z_initialsize = 0);
    CI_scheme& operator= (const CI_scheme&);
    // Optimise copy assignment to only copy filter state

    void init ();
    void update ();
    Float predict (Linrz_predict_model& f);
    Float observe_innovation (Linrz_uncorrelated_observe_model& h, const FM::Vec& s);
    Float observe_innovation (Linrz_correlated_observe_model& h, const FM::Vec& s);

    virtual Float Omega(const FM::SymMatrix& Ai, const FM::SymMatrix& Bi, const FM::SymMatrix& A)
    // Determine norm Omega 0..1 for the CI combination
    // Default norm is the fixed value 0.5
    {
        return 0.5;
    }

public:                     // Exposed Numerical Results
    FM::SymMatrix S, SI;        // Innovation Covariance and Inverse

protected:                  // allow fast operation if z_size remains constant
    std::size_t last_z_size;
    void observe_size (std::size_t z_size);
};


}//namespace
#endif