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Data association problems posed as multidimensional assignment problems: numerical simulations

机译:多维分配问题带来的数据关联问题:数值模拟

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Abstract: The central problem in multitarget-multisensor tracking is the data association problem of partitioning the observations into tracks and false alarms so that an accurate estimate of the true tracks can be recovered. Many previous and current methodologies are based on single scan processing, which is real-time, but often leads to a large number of partial and incorrect assignments, and thus incorrect track identification. The fundamental difficulty is that data association decisions once made are irrevocable. Deferred logic methods such as multiple hypothesis tracking allow correction of these misassociations and are thus considered to be the method for tracking a large number of targets. The corresponding data association problems are however NP-hard and must be solved in real-time. Such algorithms have been developed in earlier work of the authors and the intent of this work is to demonstrate the efficiency and robustness on a class of tracking problems.!13
机译:摘要:多目标多传感器跟踪中的中心问题是将观察结果分为跟踪和错误警报的数据关联问题,以便可以恢复对真实跟踪的准确估计。许多先前和当前的方法都是基于实时的单次扫描处理,但是通常会导致大量的部分和不正确的分配,从而导致不正确的轨道识别。根本的困难在于,一旦做出数据关联决定就无法撤销。诸如多重假设跟踪之类的递延逻辑方法可以纠正这些错误关联,因此被认为是用于跟踪大量目标的方法。但是,相应的数据关联问题是NP难题,必须实时解决。这种算法是在作者的早期工作中开发的,并且这项工作的目的是证明一类跟踪问题的效率和鲁棒性!13

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