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#include "../include/kalmanfilter.hpp"
// Private----------------------------------------------------------------------
void KalmanFilter::matrixInit() {
state_vector.setZero(n);
state_covariance.setZero(n, n);
state_transition_M = MatrixXf::Zero(n, n);
control_input_M = MatrixXf::Zero(n, p);
I = MatrixXf::Identity(n, n);
measurement_M.setIdentity(m, n); // Setup Measurement Matrix
process_noise_covariance = MatrixXf::Zero(n, n);
measurement_covariance = MatrixXf::Zero(m, m);
// Setup State Transition Matrix
state_transition_M << 1.0, dt,
0.0, 1.0;
// Setup Control Input Matrix
control_input_M << 0.5 * std::pow(dt, 2), // (Linear Displacement Eq.)
dt;
// Setup Process-Noise Covariance
process_noise_covariance(0,0) = 0.01;
process_noise_covariance(1,1) = 0.1;
// Setup Measurement Covariance
measurement_covariance << 0.1;
}
void KalmanFilter::updateMatrices() {
state_transition_M(0, 1) = dt;
control_input_M(0, 0) = 0.5 * std::pow(dt, 2);
control_input_M(1, 0) = dt;
}
void KalmanFilter::prediction(VectorXf control_vec) {
state_vector = (state_transition_M * state_vector) + (control_input_M * control_vec);
state_covariance = (state_transition_M * (state_covariance * state_transition_M.transpose())) + process_noise_covariance;
}
void KalmanFilter::update(VectorXf measurement) {
// Innovation
VectorXf y = measurement - (measurement_M * state_vector);
// Residual/Innovation Covariance
MatrixXf S = (measurement_M * (state_covariance * measurement_M.transpose())) + measurement_covariance;
// Kalman Gain
MatrixXf K = (state_covariance * measurement_M.transpose()) * S.inverse();
// Update
state_vector = state_vector + (K * y);
state_covariance = (I - (K * measurement_M)) * state_covariance;
}
// Public----------------------------------------------------------------------
KalmanFilter::KalmanFilter() {
}
KalmanFilter::KalmanFilter(int state_dim, int control_dim, int measurement_dim, double dt)
: n(state_dim), p(control_dim), m(measurement_dim), dt(dt) {
matrixInit();
}
bool KalmanFilter::setInitialState(VectorXf state_vec, MatrixXf state_cov) {
if (state_vec.size() != n || state_cov.rows() != n) {
std::cout << "Error: Max State & Covariance Dimension should be " << n << std::endl;
return false;
}
state_vector = state_vec;
state_covariance = state_cov;
return true;
}
VectorXf KalmanFilter::run(VectorXf control, VectorXf measurement, double _dt) {
if (control.size() != p || measurement.size() != m) {
std::cout << "Error: Control Vector Size should be "<< p
<< " Measurement Vector Size should be " << m << std::endl;
return state_vector;
}
dt = _dt;
updateMatrices();
prediction(control);
update(measurement);
return state_vector;
}
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