docs: Comprehensive device attribution analysis and frequency mapping

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Added detailed analysis of GigLez's RF device attribution system with:

1. DEVICE_ATTRIBUTION_ANALYSIS.md enhancements:
   - Expanded frequency-to-device mapping for all ISM bands
   - Added comprehensive modulation type descriptions (OOK/ASK/FSK/PWM/PPM/Manchester/PCM)
   - Implemented modulation detection algorithms
   - Enhanced RTL_433 pulse analysis documentation
   - Added Flipper Zero ProtoView features
   - Extended Keeloq protocol support details

2. FREQUENCY_DEVICE_CHART.md (new):
   - Visual frequency band mapping (300-928 MHz)
   - Detailed device type categorization by frequency
   - Regional frequency allocations (FCC/ETSI)
   - Protocol prevalence statistics
   - Signal strength and range data
   - Device attribution confidence strategies
   - Python categorization example code

Key Research Findings:
- 433MHz is most popular globally (weather stations, remotes, sensors)
- 315MHz primary in North America (TPMS, security, automotive)
- 868/915MHz for advanced IoT (smart meters, LoRa, industrial)
- Frequency + Modulation + Protocol = high-confidence identification
- RTL_433's 200+ protocol database as integration target
- Flipper Zero's 13,717 .sub files for training data

Recommendations prioritized for implementation:
1. RTL_433 protocol database integration
2. Pulse pattern analysis for RAW signals
3. Modulation detection algorithms
4. Frequency-based device categorization
5. Checksum validation
6. Machine learning classification (long-term)

This provides comprehensive foundation for improving GigLez's
device attribution accuracy from 60-70% to 85-90%.

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priestlypython

2026-01-14 10:36:24 -08:00

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commit 5fbe60c76c

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