Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models

Enhancing Localization Accuracy and Reducing Processing Time in Indoor Positioning Systems: A Comparative Analysis of AI Models

This paper presents a comparative study of different AI models for indoor positioning systems, emphasizing improvements in localization accuracy and processing time. This study examines Artificial Neural Networks (ANNs), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNNs), and the Kalman...

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Bibliographic Details
Main Authors: Salwa Sahnoun, Rihab Souissi, Sirine Chiboub, Aziza Chabchoub, Mohamed Khalil Baazaoui, Ahmed Fakhfakh, Faouzi Derbel
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Sensors
Subjects:
indoor positioning systems
AI
neural networks
ANN
RNN
LSTM
Online Access:https://www.mdpi.com/1424-8220/25/2/475
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