ReviewSensors (Basel, Switzerland)2025
Indoor Localization Methods for Smartphones with Multi-Source Sensors Fusion: Tasks, Challenges, Strategies, and Perspectives.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Master Mix Localization Algorithm for Autonomous Systems in Indoor Environments.Entropy (Basel, Switzerland) · 2026Article
- Rapid Radio Map Construction via Vehicle-Based SLAM in GNSS-Denied Environments.Sensors (Basel, Switzerland) · 2026Article
- Motion-State-Aware Adaptive Step-Length Smartphone PDR for GPS-Denied Pedestrian Localization.Sensors (Basel, Switzerland) · 2026Article
- Wireless Communication-Based Indoor Localization with Optical Initialization and Sensor Fusion.Sensors (Basel, Switzerland) · 2026Article
- Axes Mapping and Sensor Fusion for Attitude-Unconstrained Pedestrian Dead Reckoning.Sensors (Basel, Switzerland) · 2026Article
- A Vision-Assisted Acoustic Channel Modeling Framework for Smartphone Indoor Localization.Sensors (Basel, Switzerland) · 2026Article
- Machine Learning Models for Indoor Positioning Using Bluetooth RSSI and Video Data: A Case Study.Sensors (Basel, Switzerland) · 2025Article
- Indoor Positioning Systems as Critical Infrastructure: An Assessment for Enhanced Location-Based Services.Sensors (Basel, Switzerland) · 2025Review
- Classification of User Behavior Patterns for Indoor Navigation Problem.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Positioning information greatly enhances the convenience of people's lives and the efficiency of societal operations. However, due to the impact of complex indoor environments, GNSS signals suffer from multipath effects, blockages, and attenuation, making it difficult to provide reliable positioning services indoors. Smartphone indoor positioning and navigation is a crucial technology for enabling indoor location services. Nevertheless, relying solely on a single positioning technique can hardly achieve accurate indoor localization. We reviewed several main methods for indoor positioning using smartphone sensors, including Wi-Fi, Bluetooth, cameras, microphones, inertial sensors, and others. Among these, wireless medium-based positioning methods are prone to interference from signals and obstacles in the indoor environment, while inertial sensors are limited by error accumulation. The fusion of multi-source sensors in complex indoor scenarios benefits from the complementary advantages of various sensors and has become a research hotspot in the field of pervasive indoor localization applications for smartphones. In this paper, we extensively review the current mainstream sensors and indoor positioning methods for smartphone multi-source sensor fusion. We summarize the recent research progress in this domain along with the characteristics of the relevant techniques and applicable scenarios. Finally, we collate and organize the key issues and technological outlooks of this field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.