Mitigating Device Heterogeneity for Enhanced Indoor Positioning System Performance Using Deep Feature Learning

Indoor Positioning Systems (IPS) are essential for delivering accurate location-based services in environments where Global Navigation Satellite Systems (GNSS) are ineffective. The Received Signal Strength Indicator (RSSI)-based IPS leverages the existing access point infrastructure to provide a cost-effective solution for indoor location determination. However, device heterogeneity, characterized by variations in hardware, sensors, and software between devices, poses a significant challenge, often degrading positioning accuracy and robustness. This study investigates the impact of device heterogeneity on IPS performance using the TUJI1 dataset, which comprises RSSI measurements collected from five different devices. After comprehensive preprocessing of the RSSI signals, the study proposes a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) framework, leveraging advanced feature extraction techniques to improve positioning accuracy and mitigate the effects of device variability. The proposed model achieves a three-dimensional (3D) mean positioning error of 2.20 meters, outperforming traditional k-Nearest Neighbors (k-NN) methods. In cross-device evaluations, the model demonstrates improved robustness, reducing positioning errors by up to 0.17 meters compared to conventional approaches. These results highlight the effectiveness of the CNN-LSTM architecture in addressing device heterogeneity, offering a scalable and efficient solution for diverse IPS environments. This study advances the field of indoor positioning by providing a robust framework capable of maintaining high accuracy in the presence of device variability, thus contributing to the development of more reliable and adaptable IPS technologies.