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Trilateration-Based Localization Algorithm Using the Lemoine Point Formulation

机译:基于亮点的三边形定位算法

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摘要

The centroid of three most closely spaced intersections of constant-range loci is conventionally used as trilateration estimate without rigorous justification. In this paper, we address the quality of trilateration intersections through range scaling factors. Several triangle centres, including centroid, incentre, Lemoine point, and Fermat point, are discussed in detail. Lemoine point (LP) is proposed as the best trilateration estimator thanks to the desired property that the total distance to three triangle edges is minimized. It is demonstrated through simulation that LP outperforms centroid localization without additional computational load. In addition, severe trilateration scenarios such as two-intersection cases are considered in this paper, and enhanced trilateration algorithms are proposed.
机译:常规情况下,恒定距离基因座的三个最紧密间隔的交点的质心通常用作三边测量估计,而没有严格的依据。在本文中,我们通过范围比例因子解决了三边形相交的质量。详细讨论了几个三角形中心,包括质心,中心,Lemoine点和Fermat点。由于期望的特性是到三个三角形边缘的总距离最小化,因此建议将Lemoine点(LP)作为最佳三边测量器。通过仿真证明,在不增加计算负荷的情况下,LP的性能优于质心定位。此外,本文还考虑了严重的三边测量情况,例如两个交叉点的情况,并提出了增强的三边测量算法。

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