covFlt.hpp

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#ifndef _BAYES_FILTER_COVARIANCE
#define _BAYES_FILTER_COVARIANCE
 
/*
 * Bayes++ the Bayesian Filtering Library
 * Copyright (c) 2002 Michael Stevens
 * See accompanying Bayes++.htm for terms and conditions of use.
 *
 * $Id$
 */
 
/*
 * Covariance Filter Scheme.
 *  Implementation of extended Kalman filter
 * 
 * To work with with Linear and Linrz models
 *  a) a state separate from covariance predict is used.
 *  b) a EKF innovation update algorithm is used.
 * Discontinuous observe models require that predict is normalised with
 * respect to the observation.
 *
 * A initial observation size may also be specified for efficiency.
 * 
 * The filter is operated by performing a
 * predict, observe
 * cycle defined by the base class
 */
#include "bayesFlt.hpp"
 
/* Filter namespace */
namespace Bayesian_filter
{
 
class Covariance_scheme : public Extended_kalman_filter
{
public:
    Covariance_scheme (std::size_t x_size, std::size_t z_initialsize = 0);
    Covariance_scheme& operator= (const Covariance_scheme&);
    // Optimise copy assignment to only copy filter state
 
    void init ();
    void update ();
 
    Float predict (Linrz_predict_model& f);
    // Standard Linrz prediction
    Float predict (Gaussian_predict_model& f);
    // Specialised 'stationary' prediction, only additive noise
 
    Float observe_innovation (Linrz_uncorrelated_observe_model& h, const FM::Vec& s);
    Float observe_innovation (Linrz_correlated_observe_model& h, const FM::Vec& s);
 
public:                     // Exposed Numerical Results
    FM::SymMatrix S, SI;        // Innovation Covariance and Inverse
    FM::Matrix W;               // Kalman Gain
 
protected:                  // Permanently allocated temps
    FM::RowMatrix tempX;
protected:                  // allow fast operation if z_size remains constant
    std::size_t last_z_size;
    void observe_size (std::size_t z_size);
};
class Covariance_scheme : public Extended_kalman_filter {…};
 
 
}//namespace
#endif