Simulation of Multi-Target Tracking
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This paper presents a simulation of multi-target tracking employing the Multiple Hypothesis Model (MHM). During simulation, MHM serves as the core methodology for tracking targets by managing uncertainty through multiple hypothesis generation and probability evaluation. The model effectively handles target uncertainties by maintaining multiple potential tracking scenarios simultaneously, yielding more accurate tracking results. The implementation typically involves hypothesis management using techniques like Kalman filters for state prediction, JPDA (Joint Probabilistic Data Association) for measurement-to-track assignment, and hypothesis pruning to maintain computational efficiency. Key algorithmic benefits include robust adaptability in handling target occlusions, clutter, and misdetections, making it suitable for diverse tracking applications such as radar systems, computer vision, and autonomous navigation.
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