    // Copyright (C) 2012  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_KALMAN_FiLTER_Hh_
    #define DLIB_KALMAN_FiLTER_Hh_

    #include "kalman_filter_abstract.h"
    #include "../matrix.h"

    namespace dlib
    {

    // ----------------------------------------------------------------------------------------

        template <
            long states,
            long measurements
            >
        class kalman_filter
        {
        public:

            kalman_filter()
            {
                H = 0;
                A = 0;
                Q = 0;
                R = 0;
                x = 0;
                xb = 0;
                P = identity_matrix<double>(states);
                got_first_meas = false;
            }

            void set_observation_model ( const matrix<double,measurements,states>& H_) { H = H_; }
            void set_transition_model  ( const matrix<double,states,states>& A_) { A = A_; }
            void set_process_noise     ( const matrix<double,states,states>& Q_) { Q = Q_; }
            void set_measurement_noise ( const matrix<double,measurements,measurements>& R_) { R = R_; }
            void set_estimation_error_covariance( const matrix<double,states,states>& P_) { P = P_; }

            const matrix<double,measurements,states>& get_observation_model (
            ) const { return H; }

            const matrix<double,states,states>& get_transition_model (
            ) const { return A; }

            const matrix<double,states,states>& get_process_noise (
            ) const { return Q; }

            const matrix<double,measurements,measurements>& get_measurement_noise (
            ) const { return R; }

            void update (
            )
            {
                // propagate estimation error covariance forward
                P = A*P*trans(A) + Q;

                // propagate state forward
                x = xb;
                xb = A*x;
            }

            void update (const matrix<double,measurements,1>& z)
            {
                // propagate estimation error covariance forward
                P = A*P*trans(A) + Q;

                // compute Kalman gain matrix
                const matrix<double,states,measurements> K = P*trans(H)*pinv(H*P*trans(H) + R);

                if (got_first_meas)
                {
                    const matrix<double,measurements,1> res = z - H*xb;
                    // correct the current state estimate
                    x = xb + K*res;
                }
                else
                {
                    // Since we don't have a previous state estimate at the start of filtering,
                    // we will just set the current state to whatever is indicated by the measurement
                    x = pinv(H)*z;
                    got_first_meas = true;
                }

                // propagate state forward in time
                xb = A*x;

                // update estimation error covariance since we got a measurement.
                P = (identity_matrix<double,states>() - K*H)*P;
            }

            const matrix<double,states,1>& get_current_state(
            ) const
            {
                return x;
            }

            const matrix<double,states,1>& get_predicted_next_state(
            ) const
            {
                return xb;
            }

            const matrix<double,states,states>& get_current_estimation_error_covariance(
            ) const
            {
                return P;
            }

            friend inline void serialize(const kalman_filter& item, std::ostream& out)
            {
                int version = 1;
                serialize(version, out);
                serialize(item.got_first_meas, out);
                serialize(item.x, out);
                serialize(item.xb, out);
                serialize(item.P, out);
                serialize(item.H, out);
                serialize(item.A, out);
                serialize(item.Q, out);
                serialize(item.R, out);
            }

            friend inline void deserialize(kalman_filter& item, std::istream& in)
            {
                int version = 0;
                deserialize(version, in);
                if (version != 1)
                    throw dlib::serialization_error("Unknown version number found while deserializing kalman_filter object.");

                deserialize(item.got_first_meas, in);
                deserialize(item.x, in);
                deserialize(item.xb, in);
                deserialize(item.P, in);
                deserialize(item.H, in);
                deserialize(item.A, in);
                deserialize(item.Q, in);
                deserialize(item.R, in);
            }

        private:

            bool got_first_meas;
            matrix<double,states,1> x, xb;
            matrix<double,states,states> P;

            matrix<double,measurements,states> H;
            matrix<double,states,states> A;
            matrix<double,states,states> Q;
            matrix<double,measurements,measurements> R;

        };

    // ----------------------------------------------------------------------------------------

    }

    #endif // DLIB_KALMAN_FiLTER_Hh_