Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint
Moving Horizon Estimation with Probabilistic Data Association for Object Tracking Considering System Noise Constraint
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DOI：
10.20965/jrm.2020.p0537
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发表时间：
2020-06
期刊：
影响因子：
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通讯作者：
Tomoya Kikuchi;K. Nonaka;K. Sekiguchi
中科院分区：
文献类型：
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作者：
Tomoya Kikuchi;K. Nonaka;K. Sekiguchi
对象跟踪得到广泛应用，并成为自动化技术中不可或缺的一部分。然而，在包含许多物体的环境中，经常会发生遮挡和错误识别。为了缓解这些问题，在本文中，我们提出了一种基于移动地平线估计并通过概率数据关联滤波器（PDAF）结合概率数据关联（MHE-PDA）的新型对象跟踪方法。由于移动视界估计（MHE）是通过数值优化完成的，因此我们可以确保估计与物理约束一致并且对异常值具有鲁棒性。通过模拟杂乱环境，与 PDAF 进行比较，验证了该方法对抗遮挡和误识别的鲁棒性。
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.