ArticleMicromachines2017
Map-Based Indoor Pedestrian Navigation Using an Auxiliary Particle Filter.
Article in Micromachines, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 54 citations in OpenAlex.
- Article
- Inertial Indoor Pedestrian Navigation Based on Cascade Filtering Integrated INS/Map Information.Sensors (Basel, Switzerland) · 2022Article
- An Indoor Navigation Algorithm Using Multi-Dimensional Euclidean Distance and an Adaptive Particle Filter.Sensors (Basel, Switzerland) · 2021Article
- An Unconventional Multiple Low-Cost IMU and GPS-Integrated Kinematic Positioning and Navigation Method Based on Singer Model.Sensors (Basel, Switzerland) · 2019Article
- Research on an Improved Method for Foot-Mounted Inertial/Magnetometer Pedestrian-Positioning Based on the Adaptive Gradient Descent Algorithm.Sensors (Basel, Switzerland) · 2018Article
- On-The-Fly Ambiguity Resolution Based on Double-Differential Square Observation.Sensors (Basel, Switzerland) · 2018Article
- Motion Constraints and Vanishing Point Aided Land Vehicle Navigation.Micromachines · 2018Article
- An INS/WiFi Indoor Localization System Based on the Weighted Least Squares.Sensors (Basel, Switzerland) · 2018Article
- Enhanced Pedestrian Navigation Based on Course Angle Error Estimation Using Cascaded Kalman Filters.Sensors (Basel, Switzerland) · 2018Article
- Noise Source Visualization Using a Digital Voice Recorder and Low-Cost Sensors.Sensors (Basel, Switzerland) · 2018Article
- Research on the Strategy of Motion Constraint-Aided ZUPT for the SINS Positioning System of a Shearer.Micromachines · 2017Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this research, a non-infrastructure-based and low-cost indoor navigation method is proposed through the integration of smartphone built-in microelectromechanical systems (MEMS) sensors and indoor map information using an auxiliary particle filter (APF). A cascade structure Kalman particle filter algorithm is designed to reduce the computational burden and improve the estimation speed of the APF by decreasing its update frequency and the number of particles used in this research. In the lower filter (Kalman filter), zero velocity update and non-holonomic constraints are used to correct the error of the inertial navigation-derived solutions. The innovation of the design lies in the combination of upper filter (particle filter) map-matching and map-aiding methods to further constrain the navigation solutions. This proposed navigation method simplifies indoor positioning and makes it accessible to individual and group users, while guaranteeing the system's accuracy. The availability and accuracy of the proposed algorithm are tested and validated through experiments in various practical scenarios.
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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.