feat: Integrate frequency-based device identification system
Integrated comprehensive device attribution system into GigLez:
1. Simple Device Matcher (src/matcher/simple_matcher.py):
- Frequency-based device categorization (315/433/868/915 MHz)
- Protocol-specific identification (Princeton, EV1527, Oregon Scientific, etc.)
- Modulation + frequency matching (OOK/FSK/ASK)
- Confidence scoring (0.4-0.95 range)
- 50+ device types covered
2. API Integration (src/api/main_simple.py):
- Device matching in upload pipeline
- Added device fields: device_name, device_category, match_confidence, match_method, device_description
- Top 5 alternative matches stored per capture
- New endpoint: GET /api/v1/captures/{id} for detail view
3. Frontend Implementation:
- Detail modal with comprehensive device information
- Device identification section with confidence bars
- Alternative matches display
- Signal, location, and metadata sections
- Keyboard (ESC) and click-outside modal closing
4. UI Enhancements (static/css/main.css):
- Modal overlay with backdrop blur
- Animated modal slide-in
- Confidence visualization (green/yellow/red bars)
- Responsive detail grid layout
- Device match cards with categories
5. JavaScript Integration:
- detail-modal.js: Comprehensive detail view renderer
- Updated map.js and search.js to use detail modal
- Removed placeholder functions
6. Utilities:
- scripts/rematch_captures.py: Re-run matcher on existing data
- Successfully re-matched 20 existing captures
Device Categories Supported:
- Consumer RF (remotes, sensors)
- Automotive (key fobs, TPMS)
- Home Automation (garage/gate openers, blinds)
- Sensors (weather stations, temperature)
- Security (door/window sensors, alarms)
- IoT (smart meters, LoRa devices)
- Industrial (SCADA, telemetry, RFID)
Match Methods:
- Protocol matching (highest confidence: 0.7-0.95)
- Frequency matching (0.4-0.7)
- Modulation + frequency matching (0.5-0.7)
Frontend now displays:
- Device name and category on map markers
- Confidence percentage
- Detailed device information modal
- Alternative device matches
- Match method explanation
All existing captures successfully identified with 60-70% confidence.
Trilltechnician
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