Kalman Filter Implementation for Target Tracking of Uniform Linear Motion

In this implementation, I have developed a Kalman filter to track objects moving with uniform linear motion. The Kalman filter is a recursive Bayesian estimator used for state variable estimation, such as target position and velocity. The program utilizes the object's position and velocity measurements to update the filter's state vector, with the filter's output generating predictions for the target's next position. Key implementation aspects include representing the state transition and observation models through matrix operations, where the state vector typically contains position and velocity components while the measurement matrix maps these to observable parameters. To enhance performance, optimization techniques were incorporated including multithreading for parallel processing of different computational tasks and leveraging matrix operation libraries to accelerate calculation processes. The core algorithm involves two main phases: prediction (propagating state estimates using the motion model) and update (correcting estimates with new measurements). Overall, this implementation serves as a practical solution for real-world applications such as autonomous driving systems and robotic navigation where efficient target tracking is essential.