Multi-Bernoulli sensor-selection for multi-target tracking with unknown clutter and detection profiles
Multi-Bernoulli sensor-selection for multi-target tracking with unknown clutter and detection profiles
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DOI：
10.1016/j.sigpro.2015.07.007
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发表时间：
2016-02-01
影响因子：
4.4
通讯作者：
Bab-Hadiashar, Alireza
中科院分区：
文献类型：
--
作者：
Gostar, Amirali K.;Hoseinnezhad, Reza;Bab-Hadiashar, Alireza
提出了一种基于多伯努利的多目标跟踪框架内的新传感器选择解决方案。该方法是专门为一般的多目标跟踪情况而设计的，没有杂波分布或检测概率的先验知识，并为此使用了新的任务驱动的目标函数。提出了该方法的逐步顺序蒙特卡罗实现，以及使用信息驱动的目标函数（Renyi 散度）制定的类似传感器选择解决方案。在具有挑战性的场景中对这两种解决方案进行比较，结果表明，虽然两种方法在基数和状态估计的准确性方面表现相似，但任务驱动的传感器选择方法要快得多。 (C) 2015 Elsevier B.V. 保留所有权利。
A new sensor-selection solution within a multi-Bernoulli-based multi-target tracking framework is presented. The proposed method is especially designed for the general multi-target tracking case with no prior knowledge of the clutter distribution or the probability of detection, and uses a new task-driven objective function for this purpose. Step-by-step sequential Monte Carlo implementation of the method is presented along with a similar sensor-selection solution formulated using an information-driven objective function (Renyi divergence). The two solutions are compared in a challenging scenario and the results show that while both methods perform similarly in terms of accuracy of cardinality and state estimates, the task-driven sensor-selection method is substantially faster. (C) 2015 Elsevier B.V. All rights reserved.