# A Survey of Indoor Localization Systems and Technologies

arXiv: https://arxiv.org/abs/1709.01015 · HTML: https://mlsanity.com/paper/1709.01015/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2017.

## Built on by (most cited first, 30 of 78)

- [CHISEL: Compression-Aware High-Accuracy Embedded Indoor Localization with Deep Learning](https://mlsanity.com/paper/2107.01192/) (2021)
- [FedHIL: Heterogeneity Resilient Federated Learning for Robust Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2307.01780/) (2023)
- [Hierarchical Multi-Building And Multi-Floor Indoor Localization Based On Recurrent Neural Networks](https://mlsanity.com/paper/2112.12478/) (2021)
- [Deep Learning Methods for Fingerprint-Based Indoor Positioning: A Review](https://mlsanity.com/paper/2205.14935/) (2022)
- [A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications](https://mlsanity.com/paper/2112.05593/) (2021)
- [STELLAR: Siamese Multi-Headed Attention Neural Networks for Overcoming Temporal Variations and Device Heterogeneity with Indoor Localization](https://mlsanity.com/paper/2312.10312/) (2023)
- [DQLEL: Deep Q-Learning for Energy-Optimized LoS/NLoS UWB Node Selection](https://mlsanity.com/paper/2108.13157/) (2021)
- [DataLoc+: A Data Augmentation Technique for Machine Learning in Room-Level Indoor Localization](https://mlsanity.com/paper/2101.10833/) (2021)
- [QuickLoc: Adaptive Deep-Learning for Fast Indoor Localization with Mobile Devices](https://mlsanity.com/paper/2104.07521/) (2021)
- [Constructing Indoor Region-based Radio Map without Location Labels](https://mlsanity.com/paper/2308.16759/) (2023)
- [AI-driven Wireless Positioning: Fundamentals, Standards, State-of-the-art, and Challenges](https://mlsanity.com/paper/2501.14970/) (2025)
- [UMotion: Uncertainty-driven Human Motion Estimation from Inertial and Ultra-wideband Units](https://mlsanity.com/paper/2505.09393/) (2025)
- [DyLoc: Dynamic Localization for Massive MIMO Using Predictive Recurrent Neural Networks](https://mlsanity.com/paper/2101.07848/) (2021)
- [Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots](https://mlsanity.com/paper/2103.01885/) (2021)
- [Multi-Output Gaussian Process-Based Data Augmentation for Multi-Building and Multi-Floor Indoor Localization](https://mlsanity.com/paper/2202.01980/) (2022)
- [Multi-Modal Recurrent Fusion for Indoor Localization](https://mlsanity.com/paper/2203.00510/) (2022)
- [GDTM: An Indoor Geospatial Tracking Dataset with Distributed Multimodal Sensors](https://mlsanity.com/paper/2402.14136/) (2024)
- [RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information](https://mlsanity.com/paper/2404.12548/) (2024)
- [VOMTC: Vision Objects for Millimeter and Terahertz Communications](https://mlsanity.com/paper/2409.09330/) (2024)
- [MetaGraphLoc: A Graph-based Meta-learning Scheme for Indoor Localization via Sensor Fusion](https://mlsanity.com/paper/2411.17781/) (2024)
- [LLM-Guided Agentic Floor Plan Parsing for Accessible Indoor Navigation of Blind and Low-Vision People](https://mlsanity.com/paper/2604.23970/) (2026)
- [Indoor Localization Under Limited Measurements: A Cross-Environment Joint Semi-Supervised and Transfer Learning Approach](https://mlsanity.com/paper/2108.02257/) (2021)
- [A Comprehensive Survey on the Cyber-Security of Smart Grids: Cyber-Attacks, Detection, Countermeasure Techniques, and Future Directions](https://mlsanity.com/paper/2207.07738/) (2022)
- [Estimating the Pose of a Euro Pallet with an RGB Camera based on Synthetic Training Data](https://mlsanity.com/paper/2210.06001/) (2022)
- [Meta-SimGNN: Adaptive and Robust WiFi Localization Across Dynamic Configurations and Diverse Scenarios](https://mlsanity.com/paper/2511.14076/) (2025)
- [iRadioDiff: Physics-Informed Diffusion Model for Indoor Radio Map Construction and Localization](https://mlsanity.com/paper/2511.20015/) (2025)
- [Identification of Dynamical Systems using Symbolic Regression](https://mlsanity.com/paper/2107.06131/) (2021)
- [Magnetic Field Sensing for Pedestrian and Robot Indoor Positioning](https://mlsanity.com/paper/2108.11824/) (2021)
- [Online Dynamic Window (ODW) Assisted Two-stage LSTM Frameworks for Indoor Localization](https://mlsanity.com/paper/2109.00126/) (2021)
- [Unsupervised Movement Detection in Indoor Positioning Systems of Production Halls](https://mlsanity.com/paper/2109.10757/) (2021)

## Abstract (arXiv)

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.