论文标题
在无线传感器网络中高精度定位的基于大型化最小化的混合定位方法
Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks
论文作者
论文摘要
本文使用四个无线电测量值 - 到达时间（TOA），到达时间差（TDOA），接收信号强度（RSS）和到达角度（AOA）研究了混合源定位问题。首先，在调用RSS和AOA模型中可进行的近似值之后，得出了混合TOA TOA-TDOA-RSS-AOA数据模型的最大似然估计（MLE）问题。然后，通过MLE提出了加权最小二乘问题，该MLE使用多数化最小化（MM）的原理解决，从而导致具有保证收敛性的迭代算法。该方法的关键特征是它提供了一个统一的框架，其中可以根据要求/应用程序实现使用这四个测量中的任何可能合并的本地化。进行了广泛的数值模拟，以研究该方法的性能。获得的结果表明，与不同网络场景下的异质测量相比，混合定位模型提高了本地化准确性，这还包括存在非视线（NLOS）误差。
This paper investigates the hybrid source localization problem using the four radio measurements - time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA). First, after invoking tractable approximations in the RSS and AOA models, the maximum likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model is derived. Then a weighted least-squares problem is formulated from the MLE, which is solved using the principle of the majorization-minimization (MM), resulting in an iterative algorithm with guaranteed convergence. The key feature of the proposed method is that it provides a unified framework where localization using any possible merger out of these four measurements can be implemented as per the requirement/application. Extensive numerical simulations are conducted to study the performance of the proposed method. The obtained results indicate that the hybrid localization model improves the localization accuracy compared to the heterogeneous measurements under different network scenarios, which also includes the presence of non-line of sight (NLOS) errors.
