arXiv 1807.048242018-07-12eess.SP

基于TDOA的随机梯度下降变量定位

TDOA-based Localization via Stochastic Gradient Descent Variants

Luis F. Abanto-Leon, Arie Koppelaar, Sonia Heemstra de Groot

摘要：

源定位在诸如无线传感器网络和物联网（IoT）的若干领域中具有至关重要的意义，其中位置信息可以用于各种目的，例如监视、监测、跟踪，到达时间差（Time Difference of Arrival，TDOA）是一个很好的...已知的定位方法是源广播信号并且多个接收器记录所发送信号的到达时间。通过计算不同接收机的时差，可以估计出声源的位置。另一方面，近年来，在文献中出现了用于$(i)$处理大数据和用于$(ii)$训练深度神经网络的新型优化算法。这些技术大多是经典随机梯度下降（SGD）的增强变体，但具有促进更快收敛的附加功能。在本文中，我们比较了经典的SGD与上述新技术的性能。此外，我们提出了一个优化程序称为RMSProp+AF，这是基于RMSProp算法，但具有将自适应的衰减因子的优点。我们通过模拟表明，所有这些技术-这是常用的机器学习领域-也可以成功地应用于信号处理问题，并能够实现改进的收敛性和稳定性。最后，它也通过仿真表明，该方法可以优于其他竞争的方法，其收敛性和稳定性都是优越的。

英文摘要：

Source localization is of pivotal importance in several areas such as wireless sensor networks and Internet of Things (IoT), where the location information can be used for a variety of purposes, e.g. surveillance, monitoring, tracking, etc. Time Difference of Arrival (TDOA) is one of the well-known localization approaches where the source broadcasts a signal and a number of receivers record the arriving time of the transmitted signal. By means of computing the time difference from various receivers, the source location can be estimated. On the other hand, in the recent few years novel optimization algorithms have appeared in the literature for $(i)$ processing big data and for $(ii)$ training deep neural networks. Most of these techniques are enhanced variants of the classical stochastic gradient descent (SGD) but with additional features that promote faster convergence. In this paper, we compare the performance of the classical SGD with the novel techniques mentioned above. In addition, we propose an optimization procedure called RMSProp+AF, which is based on RMSProp algorithm but with the advantage of incorporating adaptation of the decaying factor. We show through simulations that all of these techniques---which are commonly used in the machine learning domain---can also be successfully applied to signal processing problems and are capable of attaining improved convergence and stability. Finally, it is also shown through simulations that the proposed method can outperform other competing approaches as both its convergence and stability are superior.

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。

摘要：

英文摘要：

相关论文