A reliable and accurate indoor localization method using phone inertial sensors

By Fan Li, Chunshui Zhao, Guanzhong Ding, Jian Gong, Chenxin Liu, Feng Zhao. Ubiquitous Computing, 2012

621 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Ocean Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/27060a0947f4d0c05f7ad3ccaf0141412f98c7c2

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In Electrical and Electronic Engineering

In Ocean Engineering

In Signal Processing

Citations in the corpus

Wi-Fi Fingerprint-Based Indoor Positioning: Recent Advances and Comparisons (2016, background): “It can be based on autocorrelation of step patterns [96] or extracting the step frequency pattern”
Electronic frog eye: Counting crowd using WiFi (2014, background): “These device-based approaches [2], [3], [4], [5], [6], [7], [8] require people to carry certain devices for surveillance, which significantly constrains the usage scope.”
Indoor Tracking: Theory, Methods, and Technologies (2015, background): “IMUs are very popular in navigation systems, particularly when they are integrated with other technologies [32].”
Evolution of Indoor Positioning Technologies: A Survey (2017, methodology): “’s study [29], the inertial sensors of a smartphone are used (accelerometer and gyroscope) as well as the compass.”
Avoiding multipath to revive inbuilding WiFi localization (2013, background): “Map based particle filtering [9, 22] relies on unique indoor walking paths such as long corridors, making them ineffective in open areas such as airport, auditorium etc.”
A portable three-dimensional LIDAR-based system for long-term and wide-area people behavior measurement (2019, methodology): “combined pedestrian dead reckoning and map-based localization.(29) Those methods can keep track the position of the person as long as they hold the sensor.”
Walk detection and step counting on unconstrained smartphones (2013, background): “However, for smartphonebased pedestrian localisation [33, 47], walk detection and step counting are much less well understood.”
Fusion of Magnetic and Visual Sensors for Indoor Localization: Infrastructure-Free and More Effective (2017, methodology): “The dead reckoning approaches [6] are simple, but they suffer from an inherit error-accumulation problem.”
Fusion of WiFi, Smartphone Sensors and Landmarks Using the Kalman Filter for Indoor Localization (2015, background): “where F, G are identity matrices and v denotes the Gaussian noise of the motion model with zero mean and covariance matrix M [31].”
A Survey on Wireless Indoor Localization from the Device Perspective (2016, methodology): “IndoorNav proposes a reliable step model for step detection and a personalization algorithm to adapt users’ stride length.”
IONet: Learning to Cure the Curse of Drift in Inertial Odometry (2018, methodology): “This approach estimates trajectories by detecting steps, estimating step length and heading, and updating locations per step (Li et al. 2012).”
Dude, where's my card?: RFID positioning that works with multipath and non-line of sight (2013, background): “Finally, a few recent proposals apply DTW to accelerometer data to match the trajectories of pedestrians and identify situations where people walk along similar paths [44, 27].”