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diff --git a/include/kalman_filter.hpp b/include/kalman_filter.hpp
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+++ b/include/kalman_filter.hpp
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+#pragma once
+#include <Eigen/Dense>
+
+using namespace Eigen;
+
+class kalman_filter {
+
+    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
+
+        float dt; // timestep
+
+        /**
+         * @brief Initialize all necessary matrices.
+         * 
+         */
+        void matrix_initialize();
+
+        /**
+         * @brief Update any existing variable elements in your State Transition 
+         * & Control Input matrices.
+         * 
+         */
+        void matrix_update();
+
+        /**
+         * @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 predict(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:
+
+        kalman_filter();
+        
+        /**
+         * @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
+         */
+        kalman_filter(int state_dim, int control_dim, int measurement_dim, float 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 state_initialize(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, float _dt);
+};