Application Background of Target Tracking Problem in Radar Data Processing

In radar data processing, target tracking represents a critically important application scenario. After detecting and recording target positional data (referred to as plots), radar systems must process these measurements to automatically form tracks and predict target positions at subsequent time steps. This paper briefly discusses the implementation of Kalman filtering for single-target trajectory prediction, demonstrating how MATLAB simulation tools can evaluate experimental performance. The Kalman filter implementation typically involves two main stages: prediction (using state transition matrices) and update (incorporating new measurements with Kalman gain calculations). Through MATLAB, we can model the system using functions like "filter" or create custom algorithms with matrix operations for covariance propagation and state correction. Furthermore, we explore methods to enhance prediction accuracy and robustness through algorithm parameter adjustments, such as tuning process noise covariance (Q) and measurement noise covariance (R) matrices. Overall, target tracking holds significant importance in radar data processing with broad application prospects, warranting in-depth research and exploration particularly through computational simulations that validate algorithmic improvements.