find3 ist ein Indoor-Positionierungs-Framework, das physische Standorte innerhalb von Gebäuden durch die Analyse drahtloser Signalmuster und Hardware-Fingerabdrücke bestimmt. Es fungiert als Machine-Learning-Standortklassifikator, der Echtzeit-Signaldaten mit einer Datenbank bekannter Fingerabdrücke abgleicht, um interne Positionen zu identifizieren.
Die Hauptfunktionen von schollz/find3 sind: Signal Fingerprinting, Location Classifiers, Indoor Positioning Systems, Navigation Frameworks, Position Classifiers, ML Localization, Local Positioning Frameworks, Signal-to-Coordinate Mappings.
Open-Source-Alternativen zu schollz/find3 sind unter anderem: 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…
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
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