异步无线网络定位的改进线性直接解法（RNL）
Improved linear direct solution for asynchronous radio network localization (RNL)
摘要：
在定位领域中，线性最小二乘解是常用的。这种解决方案相比，非线性求解器更受噪声的影响，但能够提供一个位置估计，而不知道任何启动条件。线性最小二乘解通过求解具有Moore-Penrose伪逆的超定方程来最小化高斯噪声。不幸的是，如果涉及到非高斯噪声，这种解决方案就失败了。该出版物提出了一种直接的解决方案，它是能够使用预过滤的数据的LPM（RNL）方程。用于线性位置估计的所使用的输入将不是原始数据，而是随时间滤波的数据，为此，该解将被称为直接解。结果表明，本文提出的对称直接解优于非对称直接解，特别是优于未预滤波的线性最小二乘解。
英文摘要：
In the field of localization the linear least square solution is frequently used. This solution is compared to nonlinear solvers more effected by noise, but able to provide a position estimation without the knowledge of any starting condition. The linear least square solution is able to minimize Gaussian noise by solving an overdetermined equation with the MoorePenrose pseudoinverse. Unfortunately this solution fails if it comes to non Gaussian noise. This publication presents a direct solution which is able to use prefiltered data for the LPM (RNL) equation. The used input for the linear position estimation will not be the raw data but over the time filtered data, for this reason this solution will be called direct solution. It will be shown that the presented symmetrical direct solution is superior to non symmetrical direct solution and especially to the not prefiltered linear least square solution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
