JRM Vol.32 No.3 pp. 537-547
doi: 10.20965/jrm.2020.p0537


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

December 24, 2019
March 26, 2020
June 20, 2020
moving horizon estimation, probabilistic data association, object tracking, occlusion, misrecognition
Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint

Tracking a target surrounded by false objects

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.

Cite this article as:
T. Kikuchi, K. Nonaka, and K. Sekiguchi, “Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint,” J. Robot. Mechatron., Vol.32, No.3, pp. 537-547, 2020.
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