Kalman Filter Source Code for Target Tracking

The source code implements a Kalman filter algorithm designed for target tracking applications. This recursive filter utilizes both position and velocity measurements to estimate the target's current state and predict its future trajectory through state prediction and update cycles. The implementation includes key mathematical operations such as state transition matrix computation, covariance propagation, and Kalman gain calculation. The simulation framework models uniformly accelerating turning targets, representing one of the most challenging scenarios in target tracking due to simultaneous changes in velocity magnitude and direction. The algorithm handles this complexity through proper motion modeling and process noise configuration. The state vector incorporates position, velocity, and acceleration components, while the measurement model processes position observations. Validation tests demonstrate successful tracking performance even under complex maneuvering conditions. The code structure includes modular functions for filter initialization, prediction steps (time update), and correction steps (measurement update). The implementation also features tuning parameters for process noise covariance (Q) and measurement noise covariance (R) to optimize tracking performance under different scenarios.