Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint
Tomoya Kikuchi, Kenichiro Nonaka, and Kazuma Sekiguchi
Tokyo City University
1-28-1 Tamazutsumi, Setagaya, Tokyo 158-8857, Japan
Object tracking is widely utilized and becomes indispensable in automation technology. In environments containing many objects, however, occlusion and false recognition frequently occur. To alleviate these issues, in this paper, we propose a novel object tracking method based on moving horizon estimation incorporating probabilistic data association (MHE-PDA) through a probabilistic data association filter (PDAF). Since moving horizon estimation (MHE) is accomplished through numerical optimization, we can ensure that the estimation is consistent with physical constraints and robust to outliers. The robustness of the proposed method against occlusion and false recognition is verified by comparison with PDAF through simulations of a cluttered environment.
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