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Just some restructuring to make the mechanical engineering student Milo angry
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@ -29,15 +29,6 @@ struct KalmanState
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*/
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class KalmanFilter
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{
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private:
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matrix F_; ///< The state transition matrix.
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matrix B_; ///< The control input matrix.
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matrix H_; ///< The observation matrix.
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matrix Q_; ///< The process noise covariance matrix.
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matrix R_; ///< The measurement noise covariance matrix.
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uint8_t n_; ///< The dimension of the state vector.
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matrix identity_; ///< The identity matrix with size of the state vector.
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public:
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/**
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* @brief Constructs a KalmanFilter object.
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@ -62,6 +53,12 @@ public:
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*/
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KalmanState predict(KalmanState state, matrix u);
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/**
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* @brief Predicts the next state of the Kalman filter, based on the current state.
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*
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* @param state The current state and uncertainty convariance matrix.
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* @return The predicted state of the Kalman filter.
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*/
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KalmanState predict(KalmanState state);
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/**
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@ -81,6 +78,14 @@ public:
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* @return The corrected state of the Kalman filter.
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*/
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KalmanState correct(KalmanState state, matrix z, matrix H, matrix R);
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private:
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matrix F_; ///< The state transition matrix.
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matrix B_; ///< The control input matrix.
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matrix H_; ///< The observation matrix.
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matrix Q_; ///< The process noise covariance matrix.
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matrix R_; ///< The measurement noise covariance matrix.
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uint8_t n_; ///< The dimension of the state vector.
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matrix identity_; ///< The identity matrix with size of the state vector.
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};
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} // namespace math
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@ -65,6 +65,7 @@ KalmanState KalmanFilter::correct(KalmanState state, matrix z)
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matrix K = state.error * H_.T() * linalg::inv(H_ * state.error * H_.T() + R_);
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// Update the state based on the measurement
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state.x = state.x + K * (z - H_ * state.x); //TODO check transpose
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// Update the error covariance matrix
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state.error = (identity_ - K * H_) * state.error;
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return state;
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