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a46af03f900451970f3b0be1a9376f9b083bf9f2
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3 Commits

Author SHA1 Message Date
leetcrypt 1b9b33fa3f feat: Phase 0 accuracy fix - category routing + confidence calibration 
Adds a rules-based device category router that classifies a signal by
frequency band + timing ratio + pulse count BEFORE the per-protocol
scoring loop, restricting the candidate set. This fixes the "everything
matches a weather sensor with 69-76% false confidence" problem.

- src/matcher/category_router.py: frequency-band + timing routing
- pattern_decoder.py: category filter, category-mismatch penalty,
  post-match spread penalty (surfaces low-discrimination cases)
- protocol_database.py: garage door / doorbell / fan controller entries
- scripts/benchmark_phase0.py: real-world-shaped benchmark

Benchmark gate: top-3 accuracy 0% -> 67% (target >=30%). 52/52 unit
tests pass. NOTE: benchmark .sub files are synthetic-from-DB-params,
so 67% is an upper bound pending real Flipper capture validation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-07-18 13:09:43 -07:00
leetcrypt 9f73595b20 feat: RTL_433 protocol database import - iteration 1/5 
- Expanded protocol database from 18 → 299 signatures (16.6x increase)
- Imported 281 protocols from RTL_433 open-source database (286 total devices)
- Created automated import script: scripts/import_rtl433_protocols.py
- Generated rtl433_protocols_imported.py with timing/frequency/modulation data
- Updated protocol_database.py to include RTL433_PROTOCOLS
- All 26 tests passing

Breakdown by category:
  - Weather: 116 protocols
  - Sensors: 36 protocols
  - TPMS: 25 protocols
  - Security: 23 protocols
  - Home Automation: 18 protocols
  - Other: 50+ protocols

Frequency coverage:
  - 433.92 MHz: 248 protocols
  - 315.00 MHz: 32 protocols
  - 915.00 MHz: 1 protocol

This provides comprehensive coverage of Sub-GHz IoT devices for accurate
identification from raw RF captures.
 2026-02-14 18:55:55 -08:00
Trilltechnician 9be14af160 Implement pattern-based decoder for single-transmission RF captures 
Added pattern-based decoding system specifically designed for short captures
from Flipper Zero and LilyGo T-Embed devices that don't have enough repetitions
for RTL_433.

## New Components:

1. **Protocol Database** (protocol_database.py)
   - 18 known RF protocol signatures
   - Categories: Weather Sensors, Garage Doors, TPMS, Doorbells, etc.
   - Timing patterns for Acurite, Oregon Scientific, LaCrosse, Nexus, etc.

2. **Pattern Decoder** (pattern_decoder.py)
   - Multi-strategy decoder using 3 approaches:
     - Timing pattern analysis (K-means clustering for SHORT/LONG pulses)
     - Statistical fingerprinting (signal characteristics)
     - Protocol library matching
   - Works with single-transmission captures (100-500 pulses)

3. **Matcher Integration** (strategies.py)
   - Added PatternBasedStrategy to matcher pipeline
   - Integrates with existing MatchResult system
   - Confidence scoring: 0.5-0.9 based on match quality

## Test Results:

**Pattern Decoder vs RTL_433 Performance:**
- RTL_433: 0% decode rate (0/8 files) - requires multiple repetitions
- Pattern Decoder: 44.4% decode rate (4/9 files) - works with single captures

**Successful Decodes:**
- Oregon Scientific weather sensors (76% confidence)
- Acurite weather stations (52% confidence)
- 24 total device matches across 4 files

## Implementation Details:

- K-means clustering for pulse width identification
- Statistical fingerprinting with mean, std, duty cycle
- Protocol database with 7 weather sensors + 11 other device types
- Confidence thresholds optimized for single-tx captures
- Fallback to sklearn K-means or percentile-based clustering

## Documentation:

- PATTERN_BASED_DECODING_PLAN.md: Complete implementation plan
- test_pattern_decoder.py: Comprehensive test suite

Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
 2026-01-14 19:22:07 -08:00