Precise WiFi Indoor Positioning using Deep Learning Algorithms

2023/07/05 by Minxue Cai, Cai, Minxue, Zihuai Lin +1
Computer Science · Engineering · #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Radio Wave Propagation Studies #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2307.02011

openalex publication_date 2023/07/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This study demonstrates a WiFi indoor positioning system using Deep Learning algorithms. A new method using fitting function in MATLAB will be utilized to compute the path loss coefficient and log-normal fading variance. To reduce the error, a new hybrid localization approach utilizing Received Signal Strength Indicator (RSSI) and Angle of Arrival (AoA) has been created. Three Deep Learning algorithms would be utilized to decrease the adverse influence of the noise and interference. This paper compares the performance of two models in three different indoor environments. The average error of our hybrid positioning model trained by CNN in the big classroom is less than 250 mm.

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