TDOA-based localization via stochastic gradient descent variants

L.F. Abanto Leon, Arie G.C. Koppelaar, S.M. Heemstra de Groot

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Abstract

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.
Original languageEnglish
Title of host publicationIEEE 88th Vehicular Technology Conference
Subtitle of host publicationVTC 2018-Fall
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages11
ISBN (Electronic)9781538663585
DOIs
Publication statusPublished - 15 Apr 2019
Event88th IEEE Vehicular Technology Conference (VTC 2018-Fall) - Chicago, United States
Duration: 27 Aug 201830 Aug 2018
Conference number: 88
http://www.ieeevtc.org/vtc2018fall/

Conference

Conference88th IEEE Vehicular Technology Conference (VTC 2018-Fall)
Abbreviated titleVTC2018-Fall
CountryUnited States
CityChicago
Period27/08/1830/08/18
Internet address

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