# STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization

arXiv: https://arxiv.org/abs/2312.10312 · HTML: https://mlsanity.com/paper/2312.10312/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 44 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [A Survey of Indoor Localization Systems and Technologies](https://mlsanity.com/paper/1709.01015/) (2017)
- [Multi-Head Attention Neural Network for Smartphone Invariant Indoor Localization](https://mlsanity.com/paper/2205.08069/) (2022)
- [FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2307.01780/) (2023)
- [VITAL: Vision Transformer Neural Networks for Accurate Smartphone Heterogeneity Resilient Indoor Localization](https://mlsanity.com/paper/2302.09443/) (2023)
- [CALLOC: Curriculum Adversarial Learning for Secure and Robust Indoor Localization](https://mlsanity.com/paper/2311.06361/) (2023)
- [Fusing Convolutional Neural Network and Geometric Constraint for Image-based Indoor Localization](https://mlsanity.com/paper/2201.01408/) (2022)

## Built on by (most cited first, 7 of 7)

- [SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks](https://mlsanity.com/paper/2407.11091/) (2024)
- [AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems](https://mlsanity.com/paper/2408.04797/) (2024)
- [DAILOC: Domain-Incremental Learning for Indoor Localization using Smartphones](https://mlsanity.com/paper/2506.15554/) (2025)
- [Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates](https://mlsanity.com/paper/2506.15559/) (2025)
- [GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices](https://mlsanity.com/paper/2507.11053/) (2025)
- [Unified Class and Domain Incremental Learning with Mixture of Experts for Indoor Localization](https://mlsanity.com/paper/2511.17829/) (2025)
- [ARMOR: Adaptive Resilience Against Model Poisoning Attacks in Continual Federated Learning for Mobile Indoor Localization](https://mlsanity.com/paper/2603.19594/) (2026)

## Abstract (arXiv)

Smartphone-based indoor localization has emerged as a cost-effective and accurate solution to localize mobile and IoT devices indoors. However, the challenges of device heterogeneity and temporal variations have hindered its widespread adoption and accuracy. Towards jointly addressing these challenges comprehensively, we propose STELLAR, a novel framework implementing a contrastive learning approach that leverages a Siamese multi-headed attention neural network. STELLAR is the first solution that simultaneously tackles device heterogeneity and temporal variations in indoor localization, without the need for retraining the model (re-calibration-free). Our evaluations across diverse indoor environments show 8-75% improvements in accuracy compared to state-of-the-art techniques, to effectively address the device heterogeneity challenge. Moreover, STELLAR outperforms existing methods by 18-165% over 2 years of temporal variations, showcasing its robustness and adaptability.