Find3

find3 is an indoor positioning framework that determines physical locations inside buildings by analyzing wireless signal patterns and hardware fingerprints. It functions as a machine learning location classifier that matches real-time signal data against a database of known fingerprints to identify internal positions.

The system includes a wireless signal fingerprinting tool used to scan hardware interfaces and collect signal strength data for location mapping. This data is maintained in a compressed relational signal database designed for fast retrieval of hardware addresses and signal strengths.

The framework covers a broad range of capabilities including passive signal scanning, data acquisition for fingerprinting, and the use of machine learning classifiers to estimate and determine precise indoor positions.

Features

Signal Fingerprinting - Provides a comprehensive system for mapping unique wireless signal intensity patterns to physical coordinates to determine indoor location.
Location Classifiers - Uses machine learning classifiers to categorize wireless signal fingerprints into specific internal physical zones.
Indoor Positioning Systems - Estimates physical positions within buildings by analyzing wireless signal patterns using machine learning classifiers.
Navigation Frameworks - Provides a framework for identifying user positions within a facility to enable indoor movement and discovery.
Position Classifiers - Implements a classifier that identifies internal positions by matching real-time signal patterns against fingerprint databases.
ML Localization - Uses machine learning classifiers to analyze wireless signal patterns and determine high-precision internal positions.
Local Positioning Frameworks - Provides a framework for determining physical locations inside buildings by analyzing wireless signal patterns and hardware fingerprints.
Signal-to-Coordinate Mappings - Implements a mapping system that associates physical coordinates with specific wireless signal strengths for reverse location lookup.
Signal Databases - Organizes hardware addresses and signal strengths in a relational database for fast coordinate retrieval.
Fingerprint Stores - Maintains a compressed relational store of hardware addresses and signal strengths for fast location lookup.
Signal Databases - Provides a compressed relational store for maintaining hardware addresses and signal strengths for fast indoor location retrieval.
Passive Signal Scanning - Collects wireless signal data from hardware interfaces passively to construct location-based fingerprint maps.

Star history

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

schollzfind3

Name: schollz/find3
Author: schollz

View on GitHub

4,788 stars368 forksGoMIT25 viewswww.internalpositioning.com/doc

Find3

Features

Signal Fingerprinting - Provides a comprehensive system for mapping unique wireless signal intensity patterns to physical coordinates to determine indoor location.
Location Classifiers - Uses machine learning classifiers to categorize wireless signal fingerprints into specific internal physical zones.
Indoor Positioning Systems - Estimates physical positions within buildings by analyzing wireless signal patterns using machine learning classifiers.
Navigation Frameworks - Provides a framework for identifying user positions within a facility to enable indoor movement and discovery.
Position Classifiers - Implements a classifier that identifies internal positions by matching real-time signal patterns against fingerprint databases.
ML Localization - Uses machine learning classifiers to analyze wireless signal patterns and determine high-precision internal positions.
Local Positioning Frameworks - Provides a framework for determining physical locations inside buildings by analyzing wireless signal patterns and hardware fingerprints.
Signal-to-Coordinate Mappings - Implements a mapping system that associates physical coordinates with specific wireless signal strengths for reverse location lookup.
Signal Databases - Organizes hardware addresses and signal strengths in a relational database for fast coordinate retrieval.
Fingerprint Stores - Maintains a compressed relational store of hardware addresses and signal strengths for fast location lookup.
Signal Databases - Provides a compressed relational store for maintaining hardware addresses and signal strengths for fast indoor location retrieval.
Passive Signal Scanning - Collects wireless signal data from hardware interfaces passively to construct location-based fingerprint maps.

Projects sharing features with Find3

These projects share indexed features with Find3. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.

schollz/find
schollz/find
5,093View on GitHub
This project is an indoor location tracking server and wireless fingerprinting engine designed to calculate the physical position of Wi-Fi enabled devices within a building. It functions as a local positioning framework that maps wireless signal strengths to physical space without the need for dedicated hardware beacons. The system utilizes a training process to collect and submit wireless signal data, establishing a baseline database of signal patterns for specific coordinates. It identifies current locations by matching real-time signals against these stored fingerprints using k-nearest nei
Gogps-trackerlocation-servicesmotion-sensors
View on GitHub5,093
kootenpv/whereami
kootenpv/whereami
5,137View on GitHub
whereami is an indoor geolocation tool and WiFi indoor positioning system designed to estimate a user's physical position within a building. It functions as a machine learning location predictor and WiFi signal scanner that maps wireless network patterns to specific geographic coordinates or room labels. The system utilizes a predictive pipeline to train models that translate wireless network fingerprints into location data. It identifies physical positions by analyzing signal strengths from available wireless infrastructure and comparing live scans against trained databases of known signal f
Pythonaccess-pointcross-platformdistance
View on GitHub5,137

Frequently asked questions

What does schollz/find3 do?

What are the main features of schollz/find3?

The main features of schollz/find3 are: Signal Fingerprinting, Location Classifiers, Indoor Positioning Systems, Navigation Frameworks, Position Classifiers, ML Localization, Local Positioning Frameworks, Signal-to-Coordinate Mappings.

Which projects share features with schollz/find3?

Projects with overlapping indexed features include: schollz/find — This project is an indoor location tracking server and wireless fingerprinting engine designed to calculate the… kootenpv/whereami — whereami is an indoor geolocation tool and WiFi indoor positioning system designed to estimate a user's physical…