Maneuver Target Tracking using IMM and Kalman Filter Algorithms

To enhance tracking precision for maneuvering targets, advanced filtering methodologies have been developed. The Interacting Multiple Model (IMM) approach employs multiple kinematic models (such as constant velocity and coordinated turn models) running in parallel to estimate target states. Each model's estimation is weighted based on mode probability calculations, with final output generated through probabilistic fusion. Kalman filtering provides recursive state estimation through prediction-correction cycles - predicting system states using motion models followed by measurement updates to refine estimates. The algorithm maintains covariance matrices to quantify estimation uncertainty. The integrated IMM-Kalman framework operates through key functions: 1. Model-conditioned filtering: Separate Kalman filters for each motion model 2. Mode interaction: Calculating mixing probabilities using Bayesian updates 3. Probability update: Mode likelihood computation based on innovation vectors 4. Estimate fusion: Combining model-conditioned estimates with mode probabilities This hybrid implementation achieves superior tracking performance during maneuver scenarios by adapting to changing target dynamics through model switching, while maintaining optimal estimation through Kalman's recursive structure. The algorithm can be implemented with kinematic state vectors [x, vx, y, vy] and measurement matrices mapping states to observable parameters.