Enhancing Wi-Fi RSS-Based Indoor Positioning under Dynamic AP Availability: Leveraging Virtual Feature Maps and Contrastive Learning

Fingerprinting is a practical technology for improving Wi-Fi-based positioning in complex indoor environments. However, the laborious and costly nature of site surveys hinders the creation of accurate fingerprints. In this paper, we propose Virtual Feature maps and Contrastive Learning-enhanced Indoor Positioning (VF-CLIP), a novel method for indoor positioning based on received signal strength (RSS), aiming at reducing the repetitive site survey overhead caused by the dynamic provisioning of access points (AP). VF-CLIP uses a deep neural network fine-tuning technique to reconstruct the fingerprints by incorporating newly detected APs. The proposed method converts raw received signal strength indicator (RSSI) queries into multiple virtual feature maps, which capture the differential similarities between the query vector and the fingerprints from virtual observational reference points. A depth-wise Transformer is then employed to learn the directional spatial relations of these virtual features. Subsequently, contrastive learning is applied to compress the features into a latent space, where the feature distribution at a reference point becomes compacted. We evaluated VF-CLIP on four public datasets and a fingerprint dataset collected within Huazhong University of Science and Technology, comparing its performance with other state-of-the-art methods. The experimental results demonstrated the effectiveness of VF-CLIP in terms of positioning accuracy and its adaptability to varying AP configurations, suggesting its potential applicability in real-world environments.