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Interval-scaling for multitarget localization

机译:多标识定位的间隔缩放

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In this paper we illustrate the potential of the generic I-SCAL framework for localization. Much like algebraic Multidimensional Scaling, which was originally utilized in other fields of science until it was identified as suitable for localization, I-SCAL is a SMACOF optimization-based generic framework which, to the best of our knowledge is here, for the first time, employed to solve the localization problem. To do so we propose to modify the rectangular objects employed in the standard I-SCAL framework with circular ones, resulting in faster and better performing algorithm in standard localization scenario. In addition it is shown that the computational complexity be further reduced by means of a vector extrapolation stage added in the optimization stage. The application of the proposed algorithm to the two standard localization scenarios here considered shows that the I-SCAL algorithm outperforms the SMACOF algorithm.
机译:在本文中,我们说明了通用I-SCAS框架的本地化的潜力。与代数多维缩放一样,最初用于其他科学领域,直到它被确定为适合本地化,I-SCAC是一种基于SMACOF的优化的通用框架,这是我们最受知识的,这是第一次,用于解决本地化问题。为此,我们建议修改标准I-SCAS框架中使用的矩形对象,循环框架,导致标准本地化方案中的速度更快,更好地执行算法。此外,结果表明,通过在优化阶段中添加的矢量外推阶段进一步降低计算复杂性。所提出的算法在这里的两个标准本地化方案中的应用显示,I-SCAS算法优于SMACOF算法。

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