Kalman Filter Algorithm Application for Target Trajectory Prediction in Radar Systems

Implementation of Kalman Filter Algorithm for Target Trajectory Prediction in Radar Applications with Code Integration

Motion Vehicle Extraction via Background Modeling and Foreground Segmentation

Extract moving vehicles using background modeling and foreground segmentation techniques, then perform nearest-neighbor association to output target trajectories. This MATLAB implementation of MeanShift motion target tracking follows these computational steps: 1. Extract target matrix rect 2. Calculate weighted histogram hist1 3. Process video frame by randomly selecting rect-sized region to compute hist2 4. Compare histogram ratios and iteratively update center Y using MeanShift's variable step size approach for rapid probability density peak convergence. These algorithms represent fundamental methods for image tracking applications.