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// Copyright (c) 2014 libmv authors.
//
// Permission is hereby granted, free of charge, to any person obtaining a copy
// of this software and associated documentation files (the "Software"), to
// deal in the Software without restriction, including without limitation the
// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
// sell copies of the Software, and to permit persons to whom the Software is
// furnished to do so, subject to the following conditions:
//
// The above copyright notice and this permission notice shall be included in
// all copies or substantial portions of the Software.
//
// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
// IN THE SOFTWARE.
#ifndef LIBMV_TRACKING_KALMAN_FILTER_H_
#include "libmv/numeric/numeric.h"
namespace mv {
// A Kalman filter with order N and observation size K.
template<typename T, int N, int K>
class KalmanFilter {
public:
struct State {
Eigen::Matrix<T, N, 1> mean;
Eigen::Matrix<T, N, N> covariance;
};
// Initialize from row-major data; convenient for constant initializers.
KalmanFilter(const T* state_transition_data,
const T* observation_data,
const T* process_covariance_data,
const T* default_measurement_covariance_data)
: state_transition_matrix_(
Eigen::Matrix<T, N, N, Eigen::RowMajor>(state_transition_data)),
observation_matrix_(
Eigen::Matrix<T, K, N, Eigen::RowMajor>(observation_data)),
process_covariance_(
Eigen::Matrix<T, N, N, Eigen::RowMajor>(process_covariance_data)),
default_measurement_covariance_(
Eigen::Matrix<T, K, K, Eigen::RowMajor>(
default_measurement_covariance_data)) {
}
KalmanFilter(
const Eigen::Matrix<T, N, N> &state_transition_matrix,
const Eigen::Matrix<T, K, N> &observation_matrix,
const Eigen::Matrix<T, N, N> &process_covariance,
const Eigen::Matrix<T, K, K> &default_measurement_covariance)
: state_transition_matrix_(state_transition_matrix),
observation_matrix_(observation_matrix),
process_covariance_(process_covariance),
default_measurement_covariance_(default_measurement_covariance) {
}
// Advances the system according to the current state estimate.
void Step(State *state) const {
state->mean = state_transition_matrix_ * state->mean;
state->covariance = state_transition_matrix_ *
state->covariance *
state_transition_matrix_.transpose() +
process_covariance_;
}
// Updates a state with a new measurement.
void Update(const Eigen::Matrix<T, K, 1> &measurement_mean,
const Eigen::Matrix<T, K, K> &measurement_covariance,
State *state) const {
// Calculate the innovation, which is a distribution over prediction error.
Eigen::Matrix<T, K, 1> innovation_mean = measurement_mean -
observation_matrix_ *
state->mean;
Eigen::Matrix<T, K, K> innovation_covariance =
observation_matrix_ *
state->covariance *
observation_matrix_.transpose() +
measurement_covariance;
// Calculate the Kalman gain.
Eigen::Matrix<T, 6, 2> kalman_gain = state->covariance *
observation_matrix_.transpose() *
innovation_covariance.inverse();
// Update the state mean and covariance.
state->mean += kalman_gain * innovation_mean;
state->covariance = (Eigen::Matrix<T, N, N>::Identity() -
kalman_gain * observation_matrix_) *
state->covariance;
}
void Update(State *state,
const Eigen::Matrix<T, K, 1> &measurement_mean) const {
Update(state, measurement_mean, default_measurement_covariance_);
}
private:
const Eigen::Matrix<T, N, N> state_transition_matrix_;
const Eigen::Matrix<T, K, N> observation_matrix_;
const Eigen::Matrix<T, N, N> process_covariance_;
const Eigen::Matrix<T, K, K> default_measurement_covariance_;
};
} // namespace mv
#endif // LIBMV_TRACKING_KALMAN_FILTER_H_
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