Application of Kalman Filter in Target Tracking with Interactive Multi-Model Approach

This paper explores the application of Kalman filters in target tracking systems. Specifically, we focus on tracking two-dimensional aerial maneuvering targets undergoing uniform motion using the Interactive Multi-Model (IMM) approach for radar-based target monitoring. The study provides a detailed explanation of the Kalman filter's working mechanism, including prediction and update cycles that estimate target position and velocity through state-space modeling. Implementation aspects cover how the IMM algorithm manages multiple motion models by calculating mode probabilities and blending state estimates using Markov transitions. The discussion extends to sensor fusion techniques where tracking data is integrated with complementary sensor inputs through covariance intersection methods, enhancing estimation accuracy. Code implementation considerations include handling process noise matrices for motion uncertainty and measurement noise covariance for sensor errors. Finally, we examine future directions in target tracking research, proposing potential investigations into adaptive filter tuning, nonlinear filtering alternatives like Unscented Kalman Filters, and machine learning-enhanced tracking paradigms.