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首页> 外文会议>Proceedings of the 13th Conference on Information Fusion >Shooting two birds with two bullets: How to find Minimum Mean OSPA estimates
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Shooting two birds with two bullets: How to find Minimum Mean OSPA estimates

机译：用两只子弹射击两只鸟：如何找到OSPA的最低平均估计值

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摘要

Most area-defense formulations follow from the assumption that threats must first be identified and then neutralized. This is reasonable, but inherent to it is a process of labeling: threat A must be identified and then threat B, and then action must be taken. This manuscript begins from the assumption that such labeling (A & B) is irrelevant. The problem naturally devolves to one of Random Finite Set (RFS) estimation: we show that by eschewing any concern of target label we relax the estimation procedure, and it is perhaps not surprising that by such a removal of constraint (of labeling) performance (in terms of localization) is enhanced. A suitable measure for the estimation of unla-beled objects is the Mean OSPA (MOSPA). We derive a general algorithm which provided the optimal estimator which minimize the MOSPA. We call such an estimator a Minimum MOSPA (MMOSPA) estimator.
机译：大多数防区的提法都是基于这样的假设，即必须首先识别威胁，然后将其消除。这是合理的，但它固有的是标记过程：必须先识别威胁A，然后识别威胁B，然后必须采取措施。该手稿是从这样的假设开始的，即这样的标签（A和B）是不相关的。问题自然地演变为随机有限集（RFS）估计之一：我们证明，通过避免关注目标标签，我们可以放宽估计程序，并且通过消除（标记）性能约束，这也许就不足为奇了（就本地化而言）。估计裸露物体的合适方法是平均OSPA（MOSPA）。我们推导了一种通用算法，该算法提供了使MOSPA最小化的最佳估计器。我们称此类估算器为最小MOSPA（MMOSPA）估算器。

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