schollz/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.

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