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Diffstat (limited to 'include/kalmanfilter.hpp')
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1 files changed, 98 insertions, 0 deletions
diff --git a/include/kalmanfilter.hpp b/include/kalmanfilter.hpp new file mode 100644 index 0000000..2c8398c --- /dev/null +++ b/include/kalmanfilter.hpp @@ -0,0 +1,98 @@ +#pragma once +#include "eigen3/Eigen/Dense" +#include <iostream> +#include <cmath> + +using namespace Eigen; + +class KalmanFilter { + + private: + + VectorXf state_vector; // x + MatrixXf state_covariance; // P + + MatrixXf state_transition_M; // F + MatrixXf control_input_M; // B + MatrixXf measurement_M; // H + + MatrixXf process_noise_covariance; // Q + MatrixXf measurement_covariance; // R + + MatrixXf I; // Identity + + int n; // State Vector Dimension + int p; // Control Vector Dimension + int m; // Measurement Vector Dimension + + double dt; // timestep + + /** + * @brief Initialize all necessary matrices. + * + */ + void matrixInit(); + + /** + * @brief Update any existing variable elements in your State Transition + * & Control Input matrices. + * + */ + void updateMatrices(); + + /** + * @brief Predict current State Vector & State Covariance + * given the current control input. + * + * @param control_vec The control input to be applied to the + * previous State Vector + */ + void prediction(VectorXf control_vec); + + /** + * @brief Correct the State Vector & State Covariance predictions + * given a related current measurement. + * + * @param measurement Current measurement + */ + void update(VectorXf measurement); + + public: + + KalmanFilter(); + + /** + * @brief Construct a new Kalman Filter object + * Set the sizes of the Filter's user inputs + * + * @param state_dim State Vector Dimension. i.e. dim(x) + * @param control_dim Control/Input Vector Dimension. i.e. dim(u) + * @param measurement_dim Measurement Vector Dimension. i.e. dim(z) + * @param dt timestep + */ + KalmanFilter(int state_dim, int control_dim, int measurement_dim, double dt); + + + /** + * @brief Optional function to set a custom initial state for the Filter. + * If not called, State Vector & State Covariance are zero-initialized + * + * @param state_vec Initial State Vector + * @param state_cov Initial State Covariance + * + * @return Whether state initialization was successful + */ + bool setInitialState(VectorXf state_vec, MatrixXf state_cov); + + /** + * @brief Perform Kalman Filter operation with given control input vector + * and measurement. + * + * @param control current control command + * @param measurement current measurement + * @param dt timestep + * + * @return Filtered state vector + */ + VectorXf run(VectorXf control, VectorXf measurement, double _dt); +};
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