论文标题
距离几何问题中的不匹配估计
Mismatched Estimation in the Distance Geometry Problem
论文作者
论文摘要
我们研究了距离几何问题（DGP）的背景下的不匹配估计。在DGP中，对于一组点，我们进行了对点之间成对距离的嘈杂测量，我们的目标是确定点的几何位置。处理成对距离的嘈杂测量值的一种常见方法是计算点的位置的最小二乘估计。但是，这些最小二乘估计值可能是次优的，因为它们不一定会最大程度地提高正确的可能性函数。在本文中，我们认为，当执行使用噪声测量的正确可能性函数的估计过程时，可以获得更准确的估计。我们的数值结果表明，最小二乘的估计值可以通过几个dB次优。
We investigate mismatched estimation in the context of the distance geometry problem (DGP). In the DGP, for a set of points, we are given noisy measurements of pairwise distances between the points, and our objective is to determine the geometric locations of the points. A common approach to deal with noisy measurements of pairwise distances is to compute least-squares estimates of the locations of the points. However, these least-squares estimates are likely to be suboptimal, because they do not necessarily maximize the correct likelihood function. In this paper, we argue that more accurate estimates can be obtained when an estimation procedure using the correct likelihood function of noisy measurements is performed. Our numerical results demonstrate that least-squares estimates can be suboptimal by several dB.
