MATLAB Implementation of Track Estimation and Data Association Using Kalman Filter and Nearest Neighbor Algorithm

This program performs track estimation using the Kalman Filter. The Kalman Filter is a widely-used state estimation algorithm that can be employed for signal filtering and prediction. In this implementation, the Kalman Filter is specifically applied to estimate target trajectories, enabling better understanding of object motion patterns. The algorithm typically involves two main steps: prediction (using system dynamics) and update (incorporating new measurements), with MATLAB code implementing state transition matrices, measurement matrices, and covariance calculations. In addition to the Kalman Filter, this program employs the Nearest Neighbor Algorithm for data association. The Nearest Neighbor Algorithm is a distance-based classification method that can be utilized for data classification and prediction tasks. Within this implementation, the algorithm is used to associate measurement data with existing tracks by calculating Euclidean distances between predicted states and new observations, selecting the closest matches for track updates. The MATLAB code likely includes distance calculation functions and association logic to handle multiple targets. Through this program, users can gain deeper insights into target motion states and perform more accurate track predictions and data analysis. The implementation demonstrates practical MATLAB coding techniques for real-time tracking systems, including handling of measurement noise and track management. Consequently, this program has broad application potential in various fields such as aerospace systems, intelligent transportation, and autonomous vehicle navigation.