ESTIMATION OF AIRCRAFT TRAJECTORY FROM ITS MOTION USING KALMAN TRACKING FILTER

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ESTIMATION OF AIRCRAFT TRAJECTORY FROM ITS MOTION USING KALMAN TRACKING FILTER

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Estimating an aircraft's trajectory from its motion using a Kalman tracking filter is a widely adopted technique in aerospace navigation and air traffic control. The Kalman filter is an optimal recursive algorithm that helps predict and refine an aircraft's position, velocity, and other state variables by processing noisy sensor measurements.

The process begins by modeling the aircraft's motion dynamics, typically using a state-space representation that includes position, velocity, and possibly acceleration. The Kalman filter operates in two main steps: prediction and update. In the prediction step, the filter uses the aircraft's motion model to project its state forward in time. This is followed by the update step, where sensor measurements (such as radar or GPS data) are incorporated to correct the predicted state and reduce uncertainty.

One of the key advantages of the Kalman filter is its ability to handle noisy and incomplete data while providing real-time estimates. It minimizes mean-squared error, making it highly effective for tracking aircraft trajectories even in dynamic environments. Extensions like the Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) can further improve accuracy when dealing with nonlinear motion models.

Applications range from autonomous flight systems to air traffic management, where precise trajectory estimation ensures safety and operational efficiency. The Kalman filter's recursive nature makes it computationally efficient, suitable for real-time tracking in high-speed scenarios.