A Lightweight CNN-Based WiFi Fingerprint Indoor Positioning Model

Abstract

To improve the positioning accuracy of indoor WiFi fingerprinting technology and reduce the number of model parameters, this paper proposes a lightweight indoor positioning model based on Convolutional Neural Network (CNN). Firstly, the received signal strength indication (RSSI) values are processed into a two-dimensional grayscale image. Then, deep separable convolutions are used for feature extraction, and the extracted features are passed through adaptive pooling