Beyond Stochastic Gradient Descent for Matrix Completion Based Indoor Localization
Source abstract
Abstract
In this paper, we propose a high accuracy fingerprint-based localization scheme for the Internet of Things (IoT). The proposed scheme employs mathematical concepts based on sparse representation and matrix completion theories. Specifically, the proposed indoor localization scheme is formulated as a simple optimization problem which enables efficient and reliable algorithm implementations. Many approaches, like Nesterov accelerated gradient (Nesterov), Adaptative Moment Estimation (Adam), Adadelta, Root Mean Square Propagation (RMSProp) and Adaptative gradient (Adagrad), have been implemented and compared in terms of localization accuracy and complexity. Simulation results demonstrate that Adam outperforms all other algorithms in terms of localization accuracy and computational complexity.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Indoor and Outdoor Localization Technologies · Sparse and Compressive Sensing Techniques · Underwater Vehicles and Communication Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
University of Carthage, Higher School of Communication of Tunis, LR-11/TIC-03 Innov’COM Laboratory, 2083 Ariana, Tunisia
Conservatoire National des Arts et Métiers, CEDRIC/ LAETITIA Laboratory, 75003 Paris, France
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